ENGAGEMENT: Full-time
WORK MODE: Remote

JOB DESCRIPTION:

Key Responsibilities
Build and refine machine learning models spanning traditional statistical/ML approaches and generative ML techniques.
Develop models for marketing analytics and risk analytics use cases (e.g., propensity, churn, survival-style models, risk scoring) as directed by the business.
Translate business problems into the right modeling approach – the client has strong in-house business/data analysts, so the specific value-add of this role is determining what kind of model best fits a given business need and data set.
Work primarily in Python for ETL and modelling purpose. DataRobot experience is a plus given the client's existing data science tooling.
Use SAS only for basic ETL when required to pull from the client's data warehouse – this is not the primary tool and deep SAS expertise is not required, as the client has a dedicated SAS team.

Must-Have Qualifications
Strong Python-based data science and machine learning skills.
Experience across both traditional ML and generative ML approaches.




Ability to translate a business problem into an appropriate modeling approach – this is judged as more important than deep domain (e.g., banking risk) specialization, since the client already has strong business analysts in-house.
Comfort working independently on assigned use cases without requiring constant in office presence.

Nice-to-Have
Familiarity with DataRobot or similar automated ML platforms.
Basic working knowledge of SAS for ETL purposes.
Prior marketing analytics or risk analytics experience, ideally in banking or financial services.

Skills Required
Python, Scikit learn, XGBoost, DataRobot.
ML modeling techniques (Statistical inferencing, logistic regression, Tree based models –Decision Tree/random forest/XGB, Cox hazard, clustering, A/B Testing, Uplift modeling etc.)
Model Monitoring & Model Bias & Explainability

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📌 Data Scientist – Marketing (India)
🏢 Hyper Lychee Labs
📍 India