Interview with Batool Haider, Data Scientist, UnitedHealth Group, Speaker at 4th Annual Global Big Data Conference Aug 30 - Sep Posted on : Jul 26 - 2016

We feature speakers at 4th Annual Global Big Data Conference Aug 30 - Sep 1 2016 to catch up and find out what he or she is working on now and what's coming next. This week we're talking to Batool Haider, Data Scientist, UnitedHealth Group.

Interview with Batool Haider

Tell us about yourself and your background.
I am working as a Data Science with the Talent Analytics Research team at United Health Group. I have received my Masters of Science in Petroleum Engineering from Stanford University and have served SUPRI-B, the Reservoir Simulation Research team in the Energy Resources Engineering department at Stanford University during my MS. Prior to this, I worked for Weatherford International Limited in the Middle East region where I was awarded with the ‘Core Behavior Award on Innovation’. I have presented my research studies in many international conferences, and have done Machine Learning projects with research teams at Stanford and Halliburton. These range from predicting cardiac arrhythmia in patients, to ranking the important precursors of high producing gas wells. I was the Lead Tutor Computation and Natural Sciences, at Stanford’s Summer Session, and the instructor of a popular workshop series at Stanford titled ‘Machine Learning in Plain English’.

What have you been working on recently?
At UHG, we have an employee population of over 200,000 people. This is a double edge sword as it is valuable capital and a major challenge. In the current dynamic market settings, employees join UHG, leave UHG, get transferred from among various divisions at UHG, get promoted/demoted, trained etc. At a high level view, this Fortune 6 Company seems to be in a continuous gush of talent wave which is swirling in many directions. How do we manage this stream? Who is going to leave the company and when? Are we hiring the right people? Are we promoting them right or are there long delays in our employees’ next career moves? These are some of the questions that we are answering trying to answer at United Group Talent Analytics Research Team. Currently we are at an early phase of our research, but the possibilities are limitless.

What are some of the best takeaways that the attendees can have from your "Workforce Talent Analytics Using Big Data: Predicting Retirement Events at “Fortune 6” Corporate Giant" talk?

The attendees will plunge into what I call the “HUMAN FACE OF BIG DATA!” We are using machine learning to understand our employees. The project that will be discussed in this session relates to Attrition Analysis of Experienced Employees at UHG. Each year we at UHG lose several years of experience when our employees aged 55+ quit.  To add to the overall all complexity, like many other major corporates, UHG can be divided into two major segments. First being the technology driven segment where dynamics of the market and talent landscape change fervently and managers are constantly looking for new talent equipped with knowledge of the latest market trends and tools. This segment thus prefers early retirements among its aged employees.  The second segment executes more traditional, yet key strategic operations. Here experience and deep understanding of UHG business is hugely valued, and would therefore prefer late retirement events so as to retain most of its experienced human capital. The goal of this study is to be able to identify the key factors that lead an employee to seek retirement and be able to predict retirement trends for the upcoming years. Methodologies, key findings and challenges will be discussed.

Any closing remarks?
Excited to see you in my session!