On Thursday, August 6th, Correlation One hosted the seventh edition of the C1 Connect Data Science @ Work series featuring Tatiana Sorokina, Solutions Director of Data Science and Artificial Intelligence at Novartis.
At Novartis, Tatiana works with different divisions to identify business areas that can benefit from AI/ML and oversees initiatives that implement these technologies across Novartis's product pipeline.
Throughout the Webinar, Tatiana shed some light on the state of digital transformation in the pharmaceutical/life sciences industry, the current and future applications of data science in life sciences/ pharma industries, and her data science career path to date.
Prior to joining Novartis, she was the Head of Data Science at Prognos. In her 'spare' time, Tatiana is an Adjunct Professor of applied AI and Marketing at Rutgers Business School and teaches data analytics at Brain Station. Tatiana holds a bachelor's degree in Economics and a master's degree in Business Analytics
Like many of our featured speakers throughout the Data Science @ Work Series, Tatiana's data career journey has been a non-linear one-- in her words, she described her career as being "all over the place", but she feels her variety of experience has allowed her to grow her confidence over time, making her a better business person and data scientist as a result.
Later in her talk, Tatiana talked about the state of digital transformation in pharma/ life sciences and highlighted that advances over the past 10 years have created an environment where AI and Machine Learning can now thrive at scale. According to Tatiana, now is a very exciting time to be a data scientist in pharma as new use cases in areas like generative chemistry, precision medicine, drug repurposing, and real world evidence are surfacing every day. She believes that data scientists' focus on these topics will help the science world reduce the costs of Research and Development significantly over years to come.
Before answering audience questions, she touched on different data science career paths at Novartis and shared 'Advice I would give to myself 10 years ago,' a quick dose of wisdom applicable to any young data scientist early in her career.
Watch the full Webinar below to hear Tatiana's full remarks on the above as well as the answers to these questions from our audience:
-As a Data Scientist in the pharmaceutical industry what does your daily routine look like? Any difference from other industries?
-When you look back in your career are there any moments you think you should have had more confidence in yourself and abilities?
-Do you collaborate with competitors by sharing data? Are there restrictions on data sharing? If so, how do you enforce them?
-How should 'generalist' Data Scientists craft their career narratives and tell their stories during the candidate experience?
If you would like to join a future Data Science @ Work session, feel free to follow C1 on Linkedin or join one of our Data Science for All communities on Meetup.
If you are interested in new data science opportunities with the firms of our Data Science @ Work speakers, please apply to C1 Connect here.
There is a transparency problem in the data talent market.
At C1 we work with thousands of data scientists, data analysts, and data engineers from around the world, and we often hear from job candidates that they are unsure how to evaluate different data career paths, do not know what skills they should focus on developing, and need some guidance on how to find their next data science job.
Across industries, companies are challenged to define the difference between a great data scientist, data analyst, and data engineer on job descriptions. This makes it difficult for candidates to understand what their day-to-day responsibilities will be, how certain jobs will impact their career trajectories, and how common job titles like 'data scientist' differ from one company to another.
This lack of transparency leads to a huge waste of time for both candidates and companies. Candidates adopt 'spray and pray' job application strategies, applying to hundreds of roles that have 'data' in their title. Talent teams are then forced to search through thousands of resumes to find great candidates who then must be triaged to the appropriate role search. Oftentimes, the interview process uncovers that though a candidate is an excellent data scientist, her goals and skills do not align with the role. This wastes the time of the applicant and Senior Data Scientists responsible for conducting late stage technical interviews.
We launched the C1 Connect Data Science @ Work webinar series to break down the communication barriers between hirers and the world's best data scientists, data analysts, and data engineers. Each week, our C1 Connect community is invited to hear directly from data leaders who share background on their career journeys, what working in their industry means practically for data professionals, and some tips for navigating the job search (and if applicable, how they can pursue opportunities with their teams).
After each session, candidates are invited to raise their hand for feature opportunities on C1 Connect by sharing their C1 Connect Datafolios- brief profiles designed to communicate the skills, roles, aspirations, and project work specifically for data professionals. Using C1 Connect's Talent Match Algorithm, we pass on qualified candidates who fit the profile for active opportunities to the proper next steps in the candidate selection process.