The instructors of the Data Analytics for Business Professionals certificate are leaders in Chicago’s analytics industry. Our instructors have extensive knowledge and experience in working with data to make evidence-based decisions—as well as industry connections that bring real-world problems to the classroom—making learning directly relevant to students’ career advancement goals.
Shreenidhi Bharadwaj, MS
VP Data and Analytics, Syndigo
Shreenidhi Bharadwaj is an educator, data enthusiast and an analytics practitioner. Currently, as M&A COE lead, he advises Private Equity/Venture Capital firms and C-level Executives on value creation, post-close synergies along with data & analytics(BI/AI) strategies focused on business outcomes. In his previous executive leadership role at Syndigo, he led the data strategy, data science, next-gen platform and M&A integrations. His expertise revolves around driving innovation, standardization, development & operationalizing machine learning models, data engineering at scale using on premise & cloud platforms, effective data visualizations, model-driven design & algorithmic thinking. He was elected to the Global Standards Architecture Board at GS1, where he worked with global industry leaders to develop standards, road maps, governance & compliance requirements relating to food services, healthcare, retail, supply chain & CPG/FMCG verticals. His experience spans multiple verticals such as Healthcare, Retail, MarTech, AdTech, EdTech, FinTech, Telecom and public safety with companies operating as startups to fortune 100.
Shreenidhi is a graduate of the MS Analytics program from The University of Chicago and BE, Electronics and Communications from Manipal Institute of Technology, India. His interests include Data Engineering, Intelligent Systems and Robotics, Machine Learning at scale, Data Visualization & Knowledge Engineering.
Anish Gera, MS
Data Science Manager, FIS
Anish Gera is a Data Science Manager at FIS. He is a Master of Science in Analytics graduate from the University of Chicago and has gained significant expertise in applying machine learning and natural language processing across financial services to solve complex business problems.
Josh Goldberg, MS
Data Scientist, Amazon
Joshua Goldberg is a Data Scientist at Amazon focusing on solving demand-forecasting related problems with statistics, machine learning, and deep learning. He has strong interests in artificial intelligence and programming, having completed a Master of Science in Analytics at the University of Chicago. He also holds a Bachelor of Science in Finance and Accounting. Prior to Amazon, Joshua was a Lead Data Scientist at Nuveen Investments and a senior research associate at Raymond James. In his free time, Joshua enjoys running, photography, and reading. He has finished two Chicago marathons and three half marathons.
Alena Lukina, MS
Data Scientist, Zurich
Alena Lukina has over four years of experience in analytical and data science fields. She currently works as a Data Scientist in the Data and Analytics department of the global insurance company, Zurich. At Zurich she applies a wide range of analytical and modeling techniques to unveil risk insights on a daily basis. She holds a Master of Science in Analytics degree from the University of Chicago as well as Honors Bachelor Degree in Economics from Moscow State University. For more than three years Lukina has participated in teaching the whole spectrum of courses offered by Data Analytics for Business Professionals program at the University of Chicago. Among the topics taught are Statistical Modeling and Machine Learning Techniques, Python, R, SQL, Tableau.
Oleg Melnikov, MS, MBA
Lead Data Scientist, ShareThis Inc.
Oleg Melnikov currently leads the data science team at ShareThis, Inc. in Palo Alto. He has decades of experience in mathematical and statistical research, teaching, finance (portfolio management and security analysis,) ad tech, databases, and software development.
His academic path started with a BS in Computer Science. Now, he holds an MS in computer science (machine learning track) from the Georgia Institute of Technology, an MS in mathematics from the University of California-Irvine (where he was in a math PhD program), an MS in statistics from Rice University, and an MBA from UCLA.
He has taught graduate-level statistical inference and statistical learning at Stanford University, graduate-level quantitative financial risk management courses at Rice, and was a lead TA for various courses in mathematics, statistics, and time series.
Rebeca Pop, MA
Media Analytics Professional
As a media analytics professional, Rebeca Pop has provided insights to large clients in the consumer package goods and automotive spaces, including Audi, VW, BMW, Unilever, and Samsung. She works as a subject matter expert on analytics and data visualization, course writer, and instructor. She holds a MA from the University of Oklahoma and a BA from the University of Bucharest. She has presented her research and won several top awards at national and international conferences, and her scholarly work has been published in the Encyclopedia of Public Relations.
Ashish Pujari, MS
Director of Analytics, GLG
Ashish Pujari is a leader in data and analytics, IT strategy, and technology consulting. As a director of analytics at GLG, he leads the design and implementation of business intelligence, predictive analytics, and visualization. Prior to joining GLG, he served as an AVP of analytics architecture for CNA Insurance, where he was responsible for insurance analytics platforms and data strategy.
Pujari specializes in big-data analytics, cloud computing, algorithm development, application and database design, decision management, and visualization technologies. He has been involved in technology consulting in finance, banking, insurance, and communications domains for clients in Europe, North America, and Asia.
He earned a Master of Science in Analytics from the University of Chicago and a BS in Electrical Engineering from the National Institute of Technology, Rourkela. His research interests include parallel and distributed systems and machine learning.