Data Scientist Sr.

Cognizant•Plano, TX

16h•Hybrid

About The Position

We’re looking for a Data Scientist with deep expertise in causal inference, experimentation, and advanced statistical modeling to help us understand not just what is happening in our data, but why. This role blends modern machine‑learning techniques with rigorous causal reasoning to drive high‑impact decisions across the business. You’ll work closely with cross‑functional teams to design experiments, build causal models, and deliver insights that directly influence product, operations, and strategic initiatives.

Requirements

Strong foundation in statistics, econometrics, or causal inference.
Hands‑on experience with causal modeling libraries (e.g., DoWhy, EconML, CausalML, PyTorch‑based causal frameworks).
Proficiency in Python and SQL.
Experience with ML techniques such as regression, tree‑based models, uplift modeling, and time-series analysis.
Ability to translate ambiguous business questions into structured causal problems.
Experience working with large datasets and cloud platforms (Databricks, Spark, or similar).

Nice To Haves

Graduate degree in Statistics, Economics, Computer Science, or related field.
Experience in manufacturing, supply chain, or industrial analytics.
Familiarity with Databricks MLflow, Delta Lake, and distributed computing.
Experience designing and analyzing A/B tests at scale.
Strong communication skills and ability to influence decision‑makers.

Responsibilities

Develop and implement causal inference models to quantify the impact of business actions, product changes, and operational interventions.
Build and maintain causal graphs (DAGs) to represent assumptions and guide analysis.
Apply advanced methods such as: Propensity score matching / weighting Instrumental variables Double Machine Learning (DML) AIPW / TMLE Synthetic controls
Partner with engineering and product teams to design and evaluate A/B tests and quasi‑experiments.
Use machine‑learning models to support causal estimation and predictive analytics.
Communicate findings clearly to technical and non-technical stakeholders.
Contribute to the development of reusable causal modeling frameworks and best practices.