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Remote posting, popular Data Scientist title, mid-level experience, and moderate brand raise applicant competition.
Core ML and Bayesian skills transfer across industries, though PAYG/fintech domain experience is beneficial.
Explicit 3–4 years requirement plus mandatory ML, Bayesian, Python, and SQL skills increases shortlisting rigidity.
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Design, build, and evaluate classical and Bayesian machine learning models for business-critical applications including classification, regression, anomaly detection, and forecasting.
Perform end-to-end data science workflow including exploratory data analysis, feature engineering, data wrangling on large datasets using Python and SQL, and collaborate with engineers to maintain data pipelines.
Develop, monitor, and communicate model performance metrics, identify degradation, recommend retraining, and translate business problems into statistical solutions for diverse stakeholders.
3–4 years of hands-on experience in data science or applied machine learning roles.
Proficiency in classical ML algorithms and frameworks (scikit-learn, XGBoost, LightGBM, CatBoost) plus Bayesian modeling tools (PyMC, PyMC-Marketing).
Strong programming skills in Python (pandas, NumPy, SciPy) and SQL including complex queries and optimization.
Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or related quantitative discipline.
Experienced operating across full ML lifecycle from data exploration to production-ready models with strong evaluation and experiment design skills.
Comfortable working with cloud data warehouses (AWS Redshift, BigQuery, Snowflake) and experiment tracking tools like MLflow or W&B.
Domain exposure to fintech, PAYG, emerging markets, causal inference or marketing mix modeling considered advantageous but not mandatory.