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 on-site 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
ABOUT US
Hyper Lychee Labs is an IT services and
📌 Data Scientist – Marketing (India)
🏢 Hyper Lychee Labs
📍 India
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