* Leading a team of five data scientists, overseeing projects encompassing experimentation, insights generation, and the development of machine learning models.* Driving roadmap development and fostering collaboration with product, engineering, and design teams to achieve operational objectives.* Leading experimentation and causal inference efforts, designing A/B tests and synthetic controls resulting in a increase in membership sign-ups and, and reduction in annual incentive budget costs.* Designed and implemented comprehensive experimentation analysis package encompassing data extraction, descriptive statistics, plotting, significance testing, sequential analysis, bootstrapping, and variance reduction techniques.* Contributing as a key member of an experimentation Subject Matter Expert (SME) group, facilitating review sessions of experimentation plans and documentation to enforce best practices.

-Led research initiatives focusing on automating patient-doctor interactions via machine learning models utilizing TensorFlow Keras.- Conducted rigorous analysis of experimental and observational data employing causal inference methodologies including propensity scores, matching techniques, and multilevel.- Provided consultation on experimental design and evaluation for new features and interventions

- Developed ethnicity admixture model, starting with ~10k highly unbalanced dataset from mixed sources with genotyped data with limited overlap and labeling issues. Used standard libraries (numpy, pandas, scikit learn, pyspark) and biostatistics libraries (admixture, plink) and in-house software. - Adapted and validated polygenic risk scores from published summary statistics to a consumer product- Built visualization tools with R/Shiny to empower the product team to make data-driven decisions about the product, such as the number of ethnicities in the product.

Developing machine learning models for genomics analysis products (mainly with Python & R)

• Building and deploying statistical models in R for predictive analytics of user behaviour.• Creating reports for models evaluation and tracking using Shiny + R Markdown.

• Analyzing marketing performance to drive actionable insights via SQL, R and Excel.• Creating Dashboards in various BI system (OBIEE, Panorma)

• Developing and validating pretest surveys and experiments. • Statistical analysis of Neurophysiologic and behavioral using Matalb, SPSS & Excel