Data Scientists are at the core of what we do. To that end, it’s very important that we have a good definition of the following: what does a Data Scientist do; how is a Data Scientist’s performance evaluated; and how does a Data Scientist progress in their career. Once you have these definitions, they can be used as the basis for all of your hiring, development, compensation, exit and promotion decisions.
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A career framework (sometimes called a career ladder) is a set of guidelines that clearly defines an employee’s title and expectations of their role. It allows them to see how they can move between roles and ensures the needs of the employee and company are being met.
In about 4 years, Uptake’s Data Science department has grown from 5 people to over 65. As a result, there was a hodgepodge of titles, but most of the team was titled “Data Scientist” regardless of different levels of seniority, skill set, and experience. Although we did a reasonably good job of aligning compensation with value added, there was no visibility into titles and advancement.
Everyone loves to call their loved ones and brag about their new promotion, but there is less visibility when someone gets an equivalent compensation bump without a title change. Further, internally to the team, we want to publicly recognize people’s accomplishments and career growth. These people can also serve as examples for others in the department to follow.
Objectivity and Inclusiveness
We really strive to make our department as inclusive as possible. By developing a career framework, this allows us to add a large degree of objectivity to our career process. The framework allows us to explicitly state how people are measured and by implication, explicitly how they are not to be measured.
Research Best Practices
To create our career ladder, we started by researching best practices. Fortunately, other companies have really done a great job with this and we took inspiration from other blog posts and publicly available materials
Avoid a Top-Down Approach
We didn’t want this to be a top to a top-down exercise just driven by upper management or HR, but instead really wanted buy-in from all levels so that the end product would really reflect the day-to-day realities of the job. At the same time, we wanted some group to be accountable for producing a usable result. Prior to this initiative, we had established a Data Science People Manager working group, which is a forum we use to discuss management best practices and implement department-wide people management initiatives regularly. We decided that this was the right group to own the process since they were the ones that were going to be having career conversations with their direct reports.
Think About Your Philosophy on Titles
Next, we needed to address the titles that we were going to offer, which were going to be the rows of the framework. Ben Horowitz has written extensively about the benefits of loose titles versus very strict titles. Summarizing Horowitz, there are two schools of thought on the subject, the Andreesen approach and the Zukerberg approach.
The Andreesen approach says titles are the cheapest thing you can offer someone, so give them whatever title they want and give yourself a leg up on the competition. The Zuckerberg approach says that titles should be strictly applied to promote fairness and ensure consistency in the organization. We definitely decided to take more of the Zukerberg approach to titles, in which we strictly define titles to boost fairness, internalize our leveling system and promote clarity within Uptake.
To further promote clarity, our VP of Platform Brad Boven and I decided we should try to sync up and have equivalent titles across our two organizations as much as possible. For example, his team has Staff Software Engineer, we have Staff Data Scientist, etc. This creates consistency in the organization. Further, we purposefully chose titles we believe are largely consistent with how other companies view similar roles to promote clarity for people joining Uptake.
Next, we assigned out the various columns to groups from our Data Science People Manager team to define. The group worked independently to populate what they felt were reasonable distinctions between the different levels in the department. We would periodically meet to ensure consistency across a given level and ensure we were sticking to consistent and mutually exclusive career factors.
Don’t Roll It Out Too Fast
After a few weeks of iteration, we felt we had something that we thought was a good starting place. At this point, we had two options. Option one would be to roll out the new framework quickly and do a massive retitling exercise for everyone in the department. However, we decided to go with option 2; take our time with this and solicit feedback from the rest of the department. Only then would we do a big rollout. The teams were great about asking provocative questions and they really held us accountable for defining behaviors that could be measured and observed.
Always Keep Improving
We didn’t stop there though. After every performance cycle, our Data Science People Management Group has met to discuss what worked and what could be improved. We have continued to iterate on the career framework based on that feedback and are currently on version 4.
For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.
“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.
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Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
In this article, we list down 50 latest job openings in data science that opened just last week.
(The jobs are sorted according to the years of experience r
Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.
Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.
Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.
Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.
**Location: **Bibinagar, Telangana
Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.
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According to a recent study on analytics and data science jobs, the number of vacancies for data science-related jobs in India has increased by 53 per cent, since India eased the lockdown restrictions. Moreover, India’s share of open data science jobs in the world has seen a steep rise from 7.2 per cent in January to 9.8 per cent in August.
Here is a list of 5 such companies, in no particular order, in India that are currently recruiting Data Scientists in bulk.
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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The buzz around data science has sent many youngsters and professionals on an upskill/reskilling spree. Prof. Raghunathan Rengasamy, the acting head of Robert Bosch Centre for Data Science and AI, IIT Madras, believes data science knowledge will soon become a necessity.
IIT Madras has been one of India’s prestigious universities offering numerous courses in data science, machine learning, and artificial intelligence in partnership with many edtech startups. For this week’s data science career interview, Analytics India Magazine spoke to Prof. Rengasamy to understand his views on the data science education market.
With more than 15 years of experience, Prof. Rengasamy is currently heading RBCDSAI-IIT Madras and teaching at the department of chemical engineering. He has co-authored a series of review articles on condition monitoring and fault detection and diagnosis. He has also been the recipient of the Young Engineer Award for the year 2000 by the Indian National Academy of Engineering (INAE) for outstanding engineers under the age of 32.
Of late, Rengaswamy has been working on engineering applications of artificial intelligence and computational microfluidics. His research work has also led to the formation of a startup, SysEng LLC, in the US, funded through an NSF STTR grant.
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