Changing Hiring for the Better
We leverage our AI & Analytics based technology and platform to offset bias in screening so women have more avenues to better opportunities. Meytier helps companies strengthen their diversity efforts and hire more qualified women.

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Careers of the Future
Getting into Data Science: What Hiring Managers Look for in Data Science Interviews
Team Meytier is excited to share this guest written blog by Afrozy Ara, director of Data Science & Analytics at Incedo Inc. Afrozy shares her unique perspective as a hiring manager on what makes a good data scientist and the important skills and qualities her and other managers seek out. Despite the disruption of the Covid-19 pandemic, the demand for Data scientists continues to explode. In 2019, LinkedIn ranked “Data Scientist” the No. 1 most promising job in the U.S. based on job openings, salary, and career advancement opportunities. This trend has strengthened in 2020 as Data science is booming and starting to replace legacy roles. As a Hiring Manager in this Industry, I see some very paradoxical sides of supply and demand. On one side, I come across many who want to build a career in Data Science being enticed by the opportunity it offers and the “sexiest job of the century” label. On the other hand, I find it very difficult to find good talent in the industry - the interview funnel has a very steep drop-off. Given the huge demand, there is a stark need for getting more Data professionals into the talent pool, and one of the best ways to make that happen is reskilling those who have an interest in this field. The Data Scientist though, is a hybrid creature - someone with expertise at the intersection of math, business and technology. Hence an unintended consequence of it is that this has become an abstract discipline which is understood well only by those who have been in the weeds of it. It also tends to get confused with adjacent disciplines like Data Engineering which is more about building the pipelines and plumbing that makes Data Science work. To add to it - the hype around AI does not help. But this is not an unsolvable problem. If you are looking for a way to pivot your career as a Data Scientist, you can build your profile and develop the skills needed to get the job. In this article, let's unpack the thought process of a hiring manager and the skills they look for while hiring for Data Science. Problem solving capability: This means that you have the ability to take any business problem, break it down into its constituent pieces, frame it right and solve it. Data scientists need to be able to look at the desired future state and define what is the problem that really needs to be solved. Most times the answer that your stakeholders think they want is not the answer that they need, hence having the ability to think clearly is unmistakably the most important skill, especially for senior roles. The thing about problem solving is that it is something that you learn gradually, through practice and observation. Consciously develop it in whatever field you are working on because shining here is a sure shot way to impress your interviewer. Basic technical skills: A vast majority of the work for data science would be understanding, exploring and extracting insights from the data. The absolute basics are SQL, Python , Statistics & Machine Learning. And being comfortable with visualization tools like Tableau also helps. Over here, don't get carried away by the hype and start with the data munging and exploration step first. Deep learning and Neural networks - though have the fancy zing to them, would come in much later. Getting the technical skills right is the easiest capability to build so if you are thinking of switching into a career in data science, this is a great place to start. Communication and storytelling: Another key aspect of the Data scientist is being able to influence decisions without authority and through collaboration. As someone closest to the data, you have exposure to the truth of what the data is telling us. However, the truth can be complex - so how do you translate it in a form which non-technical teams can understand? Other times, the answer you get is not the answer that the business was expecting to hear. Communicating effectively to executives so that they understand patterns or truth the data is revealing, irrespective of what the original hypothesis was is also a key skill Data Scientists need to develop to be successful in this role. Understanding Insights: this means being able to look at the data and extract insights from it, regardless of what platform or technology you are using. And connecting that Insight to the problem to eventually make a business impact. Lack of understanding of Insights is a key gap I observe in many who are attempting to crossover to the Data Science side. Having a strong domain and data orientation is an important way to overcome this gap. Take a dataset and pry it apart - what is the story that this data is telling? What are the insights you can generate from this data? Are there any inherent biases in this dataset? Every domain has a unique shape and characteristics of the data and what insights mean in that particular industry. Being able to understand the nuances of data, and how it would translate to business impact is critical for any Data science role Data Tooling: as companies increasingly move towards analytics platforms and away from ad-hoc solving, this is something that would differentiate the good and the exceptional. Understanding the data tooling for full stack analytics and personalization capabilities and being someone who can bring together the Data Insights and the Engineering aspects of it would be super valuable. There are typically two approaches to Data tooling i.e All in one platforms like Amazon Sagemaker or best-of-breed products like Snowflake, Kafka etc which are focused on one aspect of the Machine Learning process. Having an understanding of the end-to-end pipeline will help you wear many hats and is a great advantage for Data Engineers moving into Data Science. This advice is targeted towards those who want to get into the technical role of a Data Scientist. If you are not technically oriented yet want to develop a career in Data, don't lose heart. There are many non-technical roles in Data which are equally critical and impactful. I wish you the best of luck in your Data Science journey!
Careers of the Future
Arpita Sur- Reflections on Diversity in Data Science and the Future of the Field
Meytier was honored to sit down with Arpita Sur, Head for Ugam's JARVIS- Cognitive Computing System. Arpita has long been a cheerleader for Meytier's mission to increase gender diversity in analytics and technology. We were deeply inspired by her commitment to her work, to learning and to lifting those around her to reach the levels she has. We're sure you'll be as inspired as we were. 1. Tell us a little bit about your journey and how you came to be where you are now. The early days of my career were spent in the exciting world of retail finance. I was fortunate to have joined the second biggest Indian bank, ICICI. Though this was a well-established infrastructure and corporate lending bank in India, they were branching into retail finance for the first time and that was the team I was recruited into. The environment had the feel of a start-up: we had to do a lot of hands-on experimenting, creating and learning. Our teams pioneered the use of statistical models in retail lending within Indian banks. With this early foundation, I went on to join Capital One in the USA, where I honed my skills in analytics. Capital One has always been well-known for bringing the power of data and statistical evidence into every aspect of decision making. After ten years in the world of retail finance, I was ready for a change. I had learnt to lead business units and apply sound credit risk decisions on the basis of analytics. I moved back to India and moved to the world of analytics consulting. I am currently working for Ugam, A Merkle Company, a leading analytics and technology services company that works across industries such as Retail & Consumer Brands, High Tech, BFSI, Distribution, and Market Research & Consulting. 2. What kind of work are you doing now? What kind of work do you see emerging in your field? At Ugam, I lead the team that develops Ugam’s JARVIS: our Cognitive Computing System. We lead the development of proprietary models and machine learning algorithms to deliver impact and solve business problems for our clients. We adopt open source algorithms, neural net architectures, computer vision models, and modify them to suit the solution that we are developing. These solutions are then hosted in delivery worklows that are leveraged to deliver insights and output to clients. Identifying new emerging fields is a tough question, because there are so many new innovations. Change is occurring so fast. For example, natural language processing and computer vision algorithms have truly come into their own in the last 10 years. It’s almost old news now, but, every day, we still hear of newer advances, be it generating pictures from text descriptions, animating a 3D character from a single picture or converting an impressionist painting into a photo. In the space of technical architecture too, just as we've wraped our heads around cloud computing, we've begun to discuss quantum computing and the advances it will bring. For all these advancements, there are wide reaching applications that will touch many different domains. Each industry will have to adapt these to their specific use case. 3. We see that you’ve evolved as the industry has evolved from business intelligence to analytics to cognitive computing, what are your tips for reinventing yourself in tech? I remember marvelling at Excel pivots and macros when I started working 20 years ago. I learnt on the job, and took training courses to become a pro at Excel, SAS, SPSS, etc. in no time. Now, 20 years later, it’s no different. I learnt R, then Python and then went on to Spark. While training courses abound in the online education industry, these would have been of no use unless I had hands-on practice on what I had learnt. Luckily, my job gives me the opportunity to do just that. So, though I have been in managerial positions for several years, I take out time to dive in and experiment with some of the newer techniques. Another great method of keeping in touch with the latest developments is to teach. For several years now, I have been teaching courses in R and Spark to budding analysts joining Ugam. Continuous upskilling is impossible unless you know what to educate yourself on. Extensive reading on new techniques and breakthroughs in the industry gives me the edge. Machine learning these days is as much about technology as it is about algorithms, and I am an avid reader on both topics. **4. This is a high growth area without a lot of women, what do you think companies can be doing to attract and retain more women?** You’ve hit on a topic close to my heart. The wonderful thing about the AI industry is that it offers so much flexibility for its workers. I moved from a regular office day job to this new work-from-home environment without a hitch. All an analyst needs to get set up is a laptop and a really good internet connection. So, this should be a haven for the women workforce, right? Unfortunately, the reality is different. The industry is still dominated by men. This is a combination of many factors, starting with school yard conditioning and cultural stereotypes, going all the way to historical perceptions that persist through in the workplace today. Change requires interventions at many levels. Companies should start by training at the leadership level (heavily male) to understand the cultural and attitudinal differences that women brings to the table. Women tend to express themselves differently, maybe a bit less aggressively, than men. That doesn’t mean they are less sure or their ideas are not worth considering. Women themselves have to be trained to recognize how they are being perceived due to the difference in their communication. They need to understand that they should assert themselves more to be taken seriously. Many women would never dream of asking for a raise, which most men take as their birthright. So, it’s not a big surprise to see the pay grade inequalities. Senior women leaders play an important role here. At Capital One, there was a really good support system with an established women’s network. Right from my Capital One days, I have taken out time to mentor women and give them feedback on these softer points. This same concept could be formalized in soft skills training. This percolates down to hiring as well. Interviewers should be trained to recognize the differences in how women and men represent themselves, both in their resumes and in interviews. Special programs can be set up to increase the diversity quotient, starting at the internship level. At the corporate level several measures are possible, allowing women the flexibility to manage their hours, creatively matching the offshoring hours requirement with the employee constraints, allowing sabbaticals, maternity leaves, etc. 5. Who helped you get to where you are now, and how do you pay it forward? My mother was a strong role model and set the foundation for my life. She always encouraged me to try out new horizons and never let me limit my goals. She held a Masters In Chemical Engineering, which was very unusual for a woman in India in her day. Even after starting a family, she continued a career in the engineering field. She was and continues to be a truly incredible life force. Mid-way through her career, she took out the time to pursue a PhD in Chem. Engg., and then switched industries at quite a late stage in her career. Watching her made me realize that it is quite possible to beat the odds if I have the courage to try. In addition, my first two managers were also instrumental in my success. My first manager was a wonderful leader who had the talent to bring out the best in people by appreciating them and helping to build up their confidence. He was great at building teams. My second manager was a woman and she taught me a lot about what it takes for a woman to succeed in a man’s world. The best way to acknowledge and honor them is to live these values every day. I try to be positive, and encourage my team members frequently. And I am never afraid to learn new things for myself. 6. How do you hire? What do you look for in an employee? When I hire people, I like to give them real life case studies. The cases can be the simplest of problems, but, it gives me the opportunity to observe many qualities of the candidate as they think through the problem at hand. The most important quality in any employee is the right attitude. I like to see bright, eager minds who are not afraid to contribute. A person who is willing to work hard and try new things would be a more valuable employee than someone who has the required skillsets but won’t have the initiative to do anything without being told. Another important thing that I look for is a learning mindset. There are times in a simulated case study (as in life), where a negative situation arises. One of our assumptions turns out to be incorrect, or a path we took turns out to be the wrong choice. Picking oneself up, brushing oneself off and, most importantly, learning from these situations is a really important skill to harness early on.
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