Senior Associate – Data Scientist

JOB PURPOSE As a Senior Associate – Data Scientist, you will design, build, and productionize advanced analytics and AI solutions that drive measurable business value. You will work closely with Data Product Owners, Data Analysts, BI Developers, and Data Quality Specialists to translate complex business challenges into data-driven models and experiments. Leveraging Python, SQL, and modern ML frameworks, you will develop scalable machine learning solutions that are explainable, governed, and aligned with organizational priorities. ROLES AND RESPONSIBILITIES Translate business problems into data science projects by defining clear hypotheses, success metrics, and validation methods. Explore, clean, and transform structured and unstructured data using Python and SQL to prepare high-quality datasets for modeling. Design, train, and evaluate machine learning models using appropriate algorithms and statistical techniques (e.g., regression, classification, clustering, NLP, forecasting). Collaborate with engineering and platform teams to productionize AI models through reproducible pipelines and CI/CD workflows. Apply model explainability, fairness, and interpretability techniques (e.g., SHAP, LIME, feature importance) to ensure transparency and accountability. Support AI governance activities, including Model Risk Management (MRM) processes, ensuring compliance and responsible use of AI. Conduct and analyze A/B tests or controlled experiments to assess model and feature performance. Work with Data Product Owners to define business outcomes, monitor model performance post-deployment, and ensure continued relevance. Collaborate with Data Quality Specialists to ensure input data meets quality, lineage, and governance standards. Communicate results effectively through visualizations, storytelling, and presentations tailored to technical and non-technical audiences. RELATED YEARS OF EXPERIENCE 5+ years of experience in data science, applied machine learning, or advanced analytics. Proven experience delivering models that have been deployed and integrated into business processes or digital products. Experience working in agile, cross-functional teams with Product Owners, Data Analysts, and Data Engineers. TECHNICAL AND INTERPERSONAL SKILLS Advanced proficiency in Python (pandas, NumPy, scikit-learn, XGBoost, LightGBM). Strong command of SQL for data extraction, transformation, and validation. Experience working in Databricks or equivalent data and ML platforms. Familiarity with deep learning frameworks (PyTorch, TensorFlow) and ML lifecycle tools (MLflow, Airflow, Docker, Kubernetes). Understanding of model explainability, ethics, fairness, and governance. Knowledge of AI governance processes and documentation standards, including Model Risk Management (MRM). Exposure to cloud platforms (Azure, Snowflake) and APIs for integrating AI models into applications. Familiarity with version control (Git/GitHub) and collaborative coding practices. Excellent communication skills to explain technical findings in clear business terms. Collaborative and curious, with a passion for continuous learning and innovation QUALIFICATION Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field. Master’s degree or higher in Data Science, Machine Learning, or Applied Statistics is an advantage.

Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field. Master’s degree or higher in Data Science, Machine Learning, or Applied Statistics is an advantage.

Translate business problems into data science projects by defining clear hypotheses, success metrics, and validation methods. Explore, clean, and transform structured and unstructured data using Python and SQL to prepare high-quality datasets for modeling. Design, train, and evaluate machine learning models using appropriate algorithms and statistical techniques (e.g., regression, classification, clustering, NLP, forecasting). Collaborate with engineering and platform teams to productionize AI models through reproducible pipelines and CI/CD workflows. Apply model explainability, fairness, and interpretability techniques (e.g., SHAP, LIME, feature importance) to ensure transparency and accountability. Support AI governance activities, including Model Risk Management (MRM) processes, ensuring compliance and responsible use of AI. Conduct and analyze A/B tests or controlled experiments to assess model and feature performance. Work with Data Product Owners to define business outcomes, monitor model performance post-deployment, and ensure continued relevance. Collaborate with Data Quality Specialists to ensure input data meets quality, lineage, and governance standards. Communicate results effectively through visualizations, storytelling, and presentations tailored to technical and non-technical audiences.