Important factors to Consider to Embark on an Al Digital Transformation Journey

AI is everywhere. We only have to look. From smartphone apps to that self-checkout queue at that neighbourhood store to that web series that got recommended to us, there is data that we as consumers provide and AI-driven personalized experiences that we receive in return.

Despite its ubiquitous presence, there are many more avenues for AI to shine through that are as yet untapped. For AI’s true potential to be fully realized, companies have to adopt AI holistically, infusing it in all lines of businesses, across both front office and back office functions, from the physical to the digital. In other words, companies have to view AI as a critical enabler of digital transformation itself.

Choosing the right use case

Leaders need to start on the AI journey with the right use case. A use case that has manual execution or at best is realized using unsophisticated software, is a significant contributor to or enabler of business, and has potential for an outsized impact (e.g. in revenue/profitability numbers, or as competitive differentiation) is a good candidate to solve for using AI.

Initially, the trendsetter in AI adoption were those scenarios that required cognitive decision making, something that humans were quite good at with little to no training but that had befuddled machines & software. That was until the breakthrough applications of a particular design of AI systems called as deep learning happened a little over a decade ago.

In process-driven sectors such as manufacturing as well as process-driven functions such as the back office of service enterprises such as banking and insurance, AI has led to tangible cost savings and increased speed of processing. This has often had ripple effects going all the way to increase in business by way of reduced turn-around times for workflows. In particular, mundane tasks such as automated paperwork processing tend to be ideally suited for AI to do well. Most companies view such automation as a necessary first step in their move towards launching digital offerings.

Getting the data in order

“Data is the new oil”, said Clive Humby, a mathematician, while working on Tesco’s loyalty card. While this quote was from 2006, the early times of big data, it’s more modern cousin meant for the AI world could very well be “Labelled data is gold dust”.

Labeling is the process of attaching well-formed, informative labels to data in a consistent manner that guides machines in their learning. When done correctly and for large enough data, modern AI algorithms (like the deep learning ones) can often learn well and produce wondrous results that seemingly demonstrate a mastery at times surpassing human ability.

Care must be taken to avoid training with unbalanced labelled data that could prejudice the learning. Common causes of imbalance are unbalanced data itself and biases in labelling, among others.

Trying it out

Actions speak louder than words. Having done the hard work to engender AI-driven alternatives in the lab, many companies hesitate to take the plunge and take action. These companies are often stuck at this stage for fear of failure or, worse, lack of perfection.

By its very definition, an AI system learns with more experience (and corresponding labelled data). It is therefore impractical to wait for the perfect system to emerge before rolling it out on the field. On the contrary, it is best practice to launch trials, first at a limited scale, gather real-world feedback, iterate, then try out at a larger scale, and so on. After a few iterations, the improvement in performance can be assessed to see how many more iterations are required and at what cost in order to make the decision of going live or going back to the drawing board.

In summary, AI is a crucial enabler of digital transformation. Companies can be more successful in their transformation agenda by choosing the right set of use cases that AI is well suited for. For the chosen scenarios, collecting adequate amounts of labelled data in the conduct of the business is necessary. Finally, for AI-infused systems, imperfect action is better than perfect inaction. After all, much like the organization itself, a learning machine does best when it continues to learn.

How To Land The Right Data Science Job: A Data Scientist Perspective

According to AIM Research, the overall adoption of Analytics and Data Science in large Indian firms is around 74.5 percent. This implies that there is a high demand for data scientists across sectors and industries. 

However, data science is not for everyone. One must be sure of the kind of role that they are getting into and whether the particular job would allow them hands-on practice or just indulge in lab experiments.

As of October 2020, 93,500 analytics jobs were available in India. However, data science skills continue to be in short supply. “While there are plenty of training options available for newcomers to the field to get up to speed and capitalise on the high demand, there are a limited number of companies that can give a meaningful exposure to data science in practice,” said Rangarajan Vasudevan, Founder and CEO of enterprise AI solutions company TheDataTeam, during an interview with Analytics India Magazine. 

Rangarajan comes with more than two decades of experience. He got into data science and big data before it became ‘fashionable.’ Prior to founding TheDataTeam, Rangarajan built data-native applications across industries and geographies. 

A Computer Science graduate from IIT Madras and University of Michigan, Rangararajan is the recipient of Distinguished Engineer (R&D) and Consulting Excellence Awards, while he was working with Teradata Corporation as a Principal Consultant, Big Data. At present, he is a Guest Professor at IIT Madras. 

As a data scientist himself, he tells us about some of the important questions that one must ask a recruiter or hiring manager before accepting a data science role in a company:

AIM: What are some of the important questions to ask a recruiter before starting a data science role in a company?

Rangarajan Vasudevan: It is very important to ask the following questions to a recruiter or hiring manager: 

  • Give examples of business problems that have been solved by the company hiring for the role using data science in the past six months.
  • What is the current data platform architecture in use– including tools for analysis and data science?
  • Is the team hiring for this role under business or IT?
  • Share the LinkedIn profiles of this role’s supervisory hierarchy starting from the manager.
  • What other data-related roles are part of the same team?

AIM: Why are the above mentioned questions so important? 

Rangarajan Vasudevan: Hands-on practice is a must to progress in this field. The questions above could be helpful to unearth whether the company hiring for the role has a healthy habit of deriving value from data or is a potential risk.

AIM: What are the few questions, from your personal experience, you wish you had asked before taking on the role of a data scientist?

Rangarajan Vasudevan: The most important is the business context in which the data science problem is formulated. Not enough questions can be asked to truly understand this. 

In my first experience as a data scientist, I was focused on the technical aspects like performance of data preparation and scaling the pipeline to operate on large data, while missing out on the nuances of why that scaling was important and what the implication of poorly performing models was on the business of that client.

The other question I missed asking early on but made it a point to ask subsequently was on whether the work I was being asked to do as a data scientist was a ‘lab experiment’ or something that was going to make its way into a live product or service. 

As I realised later in my career, understanding this distinction was crucial to align work to expectations. A work place that offers lab experiments tends to provide avenues for rapid exploration. Whereas, one that has a live application of the data science solution more often than not provides concrete opportunities to put in practice what was learnt and see in reality what works and what doesn’t.

AIM: What are the few things one should be mindful of when taking the data science route? 

Rangarajan Vasudevan: I would strongly encourage aspiring data scientists to choose the first item over the second in this checklist since, in the long run, the latter matters a lot lesser than new data scientists think:

  • Business context over tools or technology.
  • Operational platforms over experimentation: the former exposes data scientists to practical problems that are very important to create a long term impact. 
  • Process or methodology over algorithms: for instance, deep learning or artificial intelligence is not as important as having a rigorous process for working through problems using data science that encourages making work output usable.
  • Mentors over brand or salary: this one too is an important life-lesson. One must try to work for a data science mentor even if it means a lesser salary than a high-paying job under a manager at a branded institution.

AIM: Share a few tips and hacks from your own experience as a data scientist. 

Rangarajan Vasudevan: Getting the data in order is the hardest part of a data-driven solution. I have seen some data scientists shy away from this work. Instead, I would recommend a data scientist to take complete ownership of the data pipeline including data collection, preparation and other engineering activities. Such an experience is a lot more valuable in practice.

Know that many facets of work are getting automated, and data science is no exception. For instance, leveraging the latest in automated machine learning (called as AutoML) is nothing to feel ashamed about, whereas in fact it can give you a strong leg up in arriving at answers much faster than anticipated. Learn to embrace such automation by focusing on how to use it to your advantage.

Finally, it is important to realise that the actual model building and model scoring steps in a typical data science-enabled solution take up the least amount of resources and effort compared to everything else that happens around it. For instance, setting up and managing the data platform, getting performance right, configuring policies to be able to coexist with other live projects, getting the data in the right place and shape (to name a few) all take up more effort and importance. 

Developing an empathy at least, if not understanding as well, about these ecosystem tasks would go a long way towards making the data science career enjoyable.

Behaviour Data Intelligence can reveal more about risk than past credit history

Corporate credit continues to be strongly linked to vintage and past credit history. In a world overturned by the pandemic, this does not cut it anymore since past models are no longer predictive of future performance. In fact, what matters most is the behaviour now.

Before Covid (BC) and After Covid (AC)

Commercial lending has been relationship driven from the early days. Regulatory mandates on information sharing as part of due diligence have indeed bring order to decision making. However, as with interpersonal relationships, it takes time and effort to build trust and gain access to non-regulated information that is also equally crucial for lenders to make more accurate decisions on whom to lend and how much. While credit bureaus have helped bridge the information gap in the last two decades, they have only increased the dependence of lending decisions on the particular sliver of insights stemming from credit history.

Unfortunately, to borrow from the mutual fund industry, past credit performance is not predictive of future credit performance. Throw in a pandemic of such epic proportions that the passage of time is itself cleaved into “Before Covid” (BC) and “After Covid” (AC) eras (witness Kotak Mahindra Bank‘s FY20 Annual Report). What we get is the scenario where financial institutions are left with credit decisioning models that have little value in the AC era since all their accumulated knowledge has been from the BC era. Moreover, since the AC era has only recently emerged, there is not much credit history to speak of on which to build afresh.

New Models

As companies begin to step away from the more traditional principles and practices of collecting customer behavioural insights. In this post-pandemic AC era, the need of the hour is for new credit decisioning and risk assessment models that incorporate an understanding of a client’s business that goes beyond its use of prior credit facilities.

Stress factors have varied wildly across sectors due to a multitude of reasons. For instance, the travel & logistics sector being hammered due to plummeting patronage. The MSME sector as a whole suffered from depressed expenditure in and poor collections from their BC-era client base.

Business is no longer “as usual” as expansion plans have been curtailed bringing down associated need for new credit and deferment of capex projects. The focus on controlling costs has brought to the fore actions pertaining to rationalizing use of working capital. The magnitude of such change too varies from industry to industry. Such a business climate favours certain types of credit products that result in specific risk accumulation.

Supply chains have become upended due to not just the pandemic but also global trade policies and regional geo-political developments. Business entities that heavily ride on cross-border trade be it for import of raw materials or export of finished goods / services are impacted significantly. For instance, beauty & wellness salons use a number of Chinese equipments and are primarily dependent on footfalls, both of which got impacted significantly in the past year.

Behaviour Intelligence

It is becoming increasingly pertinent for businesses to utilise advanced tools that are capable of providing more intuitive and tangible information about customers. The need of the hour is holistic information about the business from the present that aids in decision making across the credit lifecycle. In fact, such information should encompass the various aspects of the business which together reflect the functioning health of the business. Much like an individual’s behaviour can be assessed by doctors for symptoms of underlying malaise, business behaviour provides crucial inputs.

The insights that are generated from behaviours is referred to as behaviour intelligence. Companies behave in different forums differently but do exhibit patterns that are useful for risk assessment. For instance, a company that has frequent tiffs with the law as witnessed by the number of cases disposed or in pendency can be considered as a riskier proposition than one which has had a low case count.

Compliance behaviours provide a number of fairly accurate risk indicators. Corporate filings with the MCA and ROC reveal structural information that is necessary in such behavioural models, but unfortunately such information is not diligently digitized in a manner that is easily readable by a machine. Artificial intelligence (AI) techniques can help extract usable information even from such notoriously difficult sources though.

Mandated filings of returns with tax authorities (e.g. GST) and labour bodies (e.g. Employee Provident Fund) provide meaningful insights in to the health of a business. For instance, frequent delays in TDS filings could mean liquidity issues, compared to prompt timely payments.

Most of all, lending institutions such as banks that also provide business operations products are sitting on a treasure trove of behavioural data. Core banking systems have been recording transactions for many years. By mining such transactions, modern AI algorithms can unearth behavioural insights that are able to systematically build up a true picture of a client’s business. By then comparing the business between the BC and AC eras, new stress factors can be rapidly surfaced resulting in both timely risk alerts as well as emergent opportunities for new business.

In summary, lenders need to look beyond past credit history to make credit decisions. Business behaviours have to be understood holistically, especially in this post-pandemic era. The good news is that lenders already have the necessary data in-house, with external data augmenting the richness as necessary. Technologies too have rapidly evolved to accelerate this new dawn of decisioning models. What remains is for the leaders in the lending institutions to change their mindset and adopt such innovation.

The author is Rangarajan Vasudevan, Founder & CEO, TheDataTeam

Businesses are struggling to create a single view of the customer

Although CDP has become a global phenomenon, various independent studies are bringing forward the challenges businesses face in building a single view of their customers. 

This new study by Forrester Consulting, yet again confirms it. 

The November 2019 study, “Getting Customer Data Management Right,” which includes insights from over 350 marketing and advertising professionals in North America and Europe, unifying disparate customer data is still a struggle with most companies who are building a single view of the customer. 

Firms with a unified view of the customer produce better business results.

As an enterprise is dependent on its customers, not understanding them well has a direct impact on business. Traditionally, customer personas have proved powerful for marketing activities. Marketers today have an opportunity to improve even more on their organizations’ customer data practices.

According to this study, only 11% of firms effectively use a wide variety of data types in a unified customer profile;

 these same leading firms are 2.5 times more likely to increase customer lifetime values as a result of unified data management.

Customer engagement data is critical to achieving superior customer experience. The study clearly states the change customer engagement personalization can bring. Companies have witnessed a

     –69% increase in the prospect engagement rates with relevant personalized targeting

  • -Close to 70% increase in customer conversion rates
  • -56% reduction in operational cost with personalization

However, companies are struggling with building a solid foundation to execute the same.

A Customer Data Platform, aka CDP, serves as precisely this solid foundation for successful personalized customer engagement initiatives. It helps build a consolidated single view of each customer live, which in turn enables businesses to keep abreast of individual customer behavior and aggregate patterns over time. Without CDPs, building a customer profile from all the disparate data sources is a cost and time-consuming activity with the need for complex system integration and data integration challenges.

For enterprises to remain competitive in this digital age, it is important to focus on creating personalized experiences using a methodical process built on a solid foundation in the form of a CDP that builds and manages unified customer profiles. What’s more, it is necessary to enrich profiles with actionable intelligence. After all, collecting data just for the sake of it doesn’t really cut it. Whereas, it is important to understand how each data point contributes to a deeper understanding of how the brand can serve customer needs.

Forbes India’s AI Innovation Round Table

Forbes India hosted AI Innovation Round Table – Envisioning innovation for an Alternative Future, in December 2019. Industry leaders and domain experts gathered under Forbes India’s stewardship in a closed-group round table in Mumbai to discuss the democratization of AI on the Indian industry and its fair use. Our CEO Rangarajan Vasudevan was invited to be a part of this discussion.                                            

 Following is the summary of his views on the topics.    

Q: Would you like to share your thoughts and experiences on how AI is accelerating innovation across the industry?

 Rangarajan Vasudevan: Setting aside the hype, AI is predominantly considered in uses that impact the bottom line.  There is also this other trend where decision-makers in the industry are interpreting AI as being applicable predominantly where human cognitive ability has called the shots so far. While this is undoubtedly one of the path-breaking applications of AI which have emerged in the last few years, there is more to AI than just trying to “see” or “speak” like humans. The foundation for good, fair AI is large volumes of clean, labeled data, which is where many enterprises in India are still maturing.

Q: How can AI drive empowerment at scale – Individuals, societies, and sustainability?
Rangarajan Vasudevan: In a broader sense, replace the word AI with technology and let’s look at how technology can drive empowerment. There are just too many examples here on the impact technology is having on these stakeholders. From a more nuanced point of view, AI is bringing to the table disruptive capabilities that call for a completely different approach to solve specific problems. Here, businesses can aim for low-hanging fruits. I can imagine, for instance, someone taking a crack at the problem of garbage sorting using the power of AI to recognize objects, something AI has matured at. AI is also extremely good at providing aid required for individuals at moments of crises by monitoring health vitals and correlating with the past medical history.

Q: How do you see AI influencing the existing skillsets of the working population?
Rangarajan Vasudevan: Technology skills are in high demand, naturally. Automation is becoming more widespread, at times, even without requiring AI. The nature of jobs is changing as well. Some of the newer skills are going to be about creating accurate data sets that are used to improve and augment. Reinventing the way an enterprise works at all levels will also require tremendous amounts of design and operational expertise.

Q: What are your thoughts on the collective responsibilities of organizations, policymakers and industry bodies in governing the use of AI for meaningful innovation?
Rangarajan Vasudevan: Data Privacy is a serious matter, especially in India where regulation is nascent. Left to themselves, organizations will likely end up prioritizing shareholder value over the common good. It is necessary for us to be more educated about how data we provide is being used and build a sense of ownership about the data. Likewise, it is essential for organizations to earn the trust of their customers in a transparent and symbiotic way. The government should facilitate but avoid over-regulation. The AI built on top must have transparency about the data utilized to produce business outcomes. By transparent, I’m referring to technological concepts of explainability, traceability, and provability.

TheDataTeam is the creator of Cadenz.ai, the market’s first Customer Intelligence Platform(CIP) that provides enterprises live behaviour intelligence about its customers using its data automatically and help personalize at scale. Coupled with employing cutting-edge AI algorithms that power the intelligence pipelines, Cadenz also supports governance natively to abide by the principles outlined above.


You can find the complete transcript here. For more information contact us at contact@thedatateam.com

My journey of becoming a data analyst at TheDataTeam

Hi, I am Anagha Gopalakrishnan, Data Analyst at TheDataTeam. I recently completed my first year here and I am delighted to share some of my journey with you. 

My journey in the field of Data Science began in the year 2018. I was in a dilemma back then about choices I should be making in my career and where would those choices take me in the coming years. Being new in the world of software, I was totally perplexed on what I should pursue. I was constantly contemplating on my strengths & weaknesses. My mind was filled with millions of questions and a whole basket of career choices was right in front of me. I was lucky enough to have my family support whichever choice I made. I then started researching about fields I found interesting and decided to pursue Post-Graduation in Data Science and Engineering. This decision gave a new horizon to my career and I landed this profile of a Data Analyst here at TheDataTeam(TDT).

What do I find challenging in my job?

I was excited to join this startup as AI was at the core of everything TDT engaged in. I immediately realized that this is the right place to start my career.

I had experienced corporate culture earlier where as a young professional the responsibility and ownership given to us are very little. There are multiple people doing similar work as us and the responsibility we receive is limited. In TDT for the first time, I was experiencing what it means to own our own work. The significant responsibility given to me at such an early stage of my career changed the way I look at work forever.

It is extremely challenging to deliver work with great accountability and ownership along with gaining customer’s confidence. I had never thought that I would be able to interact with customers in the first year of my joining. This is exactly what makes my work dynamic for me.

This mode of working helped me gain confidence in my work and understand the complete workflow of the delivery process.

Why do I love it here?

Being able to work with people with different skills and entirely different experiences is the part I look forward to every day. It has enhanced my way of thinking and inspired me to keep up. This has completely washed off the age-old ‘not doing things that are not in my job description’ attitude. Now I am always ready to work on something new and learn something that I do not know. 

One always needs to push one’s limits and come out of the comfort zone to grow in a career. This is the most rewarding part for me.

I also love the fact that we as a team discuss everything under the sun and bond at a deeper level.

In a parallel universe, what’s a different career I would have pursued?  

Teaching, yes if there was a parallel universe, I would have loved to become a teacher. Being able to share my knowledge, will keep me on learning mode continuously is what I believe. 

What’s something people would be surprised to learn about me?

I started dancing when I was four, spending sleepless nights and extended hours in learning new choreographies always excited me. It was always fun to dance with my friends during school, college and even at my workplace.  

I am pumped up to start my second year with newer projects and challenges.

TheDataTeam brings on board enterprise software veteran,Pushkaraj Kale as Co-Founder & COO

TheDataTeam, an AI solutions company focusing on smoothening customer friction, announced today it had brought on board business veteran, Pushkaraj Kale, as Chief Operating Officer. He reports to Rangarajan Vasudevan, the company’s Founder & CEO, and joined the Board as Co-Founder & Director.

Pushkaraj is a technology veteran with over 27 years of experience in establishing and operating large enterprise businesses. Most recently, he had built & led the BFSI sector into the fastest growing business at Microsoft India. Prior to that he was Regional Director for CA Inc and worked with L&T Infotech. He is recognized across the enterprise software industry for driving sales effectiveness & operational excellence and stands out as a leader who builds long-lasting relationships with clients, peers, and team members.

On appointment, Pushkaraj said ”I am thrilled to be a part of TheDataTeam. I have been advising various AI startups for some time now but what differentiates TheDataTeam from others is Ranga’s vision for the company, their product Cadenz.ai, & the culture he is building. These are the ideal seeds of longevity for any company. Everyone here has a strong client-centric obsession and always keeps the customer at the core of whatever they do. Most enterprise customers, globally, love to work with them. They are well-positioned for the next level of growth and this was the perfect time to get my hands deep and be a part of that journey” 

Rangarajan Vasudevan, Founder & CEO said, “We are at a crucial stage in our company history where the impending launch of our platform Cadenz.ai in multiple geographies requires a strong focus on the top line, while the current global climate necessitates an optimization of the bottom-line. Pushkar brings to TheDataTeam this critical expertise to balance and achieve these twin objectives in a very short period. I am extremely delighted that he has come on board to accelerate this wonderful journey of solving customer friction problems for large enterprises using the power of AI.”

Here are 5 tips straight from our recruiters to help your job application stand out

Welcome

Job search is an exhausting experience! If you want to thrive in an upcoming field, then the chances of you missing a good opportunity due to your applications going unnoticed are high. We receive countless resumes every day at TheDataTeam(TDT)  And we’ve seen it all—the good and the not-so-good. Therefore our recruiters decided to make this process less daunting for you.

Do you want to know what makes us notice a resume?

Here’s one example: Scanning through new applications for an entry-level marketing role, we came across an interesting file that read: “OPEN ME.” Of course, our curiosity got the best of us, and we opened the elaborate, flashy, fun PowerPoint presentation containing the candidate’s information and work history. If you are a data scientist, you can write “Yourname_for_TheDataTeam”. Be creative but don’t miss the point.

By putting in the extra effort to make the resume stand out, the candidate quickly gets attention—and the interview. And we happily route the resume to the hiring manager. Here are some tips which will help you get noticed by the TheDataTeam recruiters.

        1. Don’t shoot out generic resumes. Personalize it for the Job Description

We receive thousands of resumes per job posting and we always appreciate a candidate who has spent time in not only understanding what we do but also in helping us navigate through his resume by mentioning the skills and experiences which are relevant. Here is a simple tip to get this right

  • Only mention relevant skills on the first page of your resume along with a competency scale. Rate your skills on a scale of 1 to 5. 5 being the highest and 1 being the lowest proficiency.

       2. Add bullet points to highlight relevant skills at the top

We have noticed that every resume has a very ineffective objective at the top. At TDT we take pride in cutting out noneffective formalities to build efficiency at everything we do. So cut out the generic objective and instead use that space to

  • Add 5 most important skills that you have which are relevant to the job you are applying for at the top competency rating as mentioned in the above point.
  • Top 3 achievements in your earlier work experiences. Please keep these relevant to the role you have applied for. Remember no irrelevant descriptions or skills on page 1!

        3. Only include detailed work experience(More than 3 lines) of relevant jobs you have done in the past

Candidates believe if they provide more description of their experiences it increases their chance of getting noticed. This is a myth. Honestly, at the recruiter level, we want to know as fast as we can that you are good for THE role you are applying. A resume is not a place to impress a recruiter, it is a document to help recruiters choose you. Here are a few tips on how you can help us choose you.

  • Mention your latest and relevant work experience first. Start from the latest one and end it with the oldest one while covering your work experience.
  • Keep the descriptions short. We would go beyond it and say write crisp bullet points and avoid cluttering resumes with unnecessary jargons.
  • Do not over-describe nonrelevant work experiences.

          4. Add hyperlinks to your resume

Hyperlinks save space on the resume and still provide the recruiter a chance to know you in-depth. The hiring manager definitely likes to know more about you. Here are some tips-

  • Keep your LinkedIn updated and then include a link. No one wants to see a LinkedIn account that’s old and dead for ages.
  • Provide links of your work if it is unavailable online like GitHub or Quora or any other platform.
  • Provide website links of your earlier companies instead of writing long descriptions of companies you worked for.

          5. Review, Reformat and proofread

This is old news, but we still receive ill-formatted resumes. This creates a poor first impression. Formatting and reviewing hardly take 15 min and we suggest spending that time before forwarding your resume. We recommend getting it reviewed from a third person who has an eye for detail.

  • Review for spelling mistakes and grammar errors. English does not matter for your ability to work. This just shows that you put effort into what you do suggesting that you will put effort into your work as well.
  • Send a pdf instead of sending word files. There are many free online word to pdf converters.
  • Proofread, proofread, proofread

We know that the current situation due to Covid-19 is causing job seekers’ anxiety and #WEAREINTHISTOGETHER. We are hiring and will continue to do so.

Know how you can  Grow your career with TheDataTeam and do work that makes a difference every day. We wish you the best of luck in your job search endeavors.

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