Assistant Vice President – Data Science

Job Purpose

As an Assistant Vice President in Data Science, your main responsibility will be to architect, develop, and deploy cutting-edge analytics and AI-driven solutions that contribute directly to business success. You will collaborate extensively with roles such as Data Product Owners, Data Analysts, BI Developers, and Data Quality Specialists to convert complex business challenges into actionable data-driven models and experiments. Utilizing technologies like Python, SQL, and current machine learning frameworks, you will build scalable, explainable, and governed AI solutions that align with company goals.

Key Responsibilities

• Transform business challenges into clear data science projects by formulating hypotheses, defining success metrics, and establishing validation approaches.
• Handle data exploration, cleansing, and transformation of both structured and unstructured datasets using Python and SQL to prepare high-quality inputs for modeling.
• Create, train, and assess machine learning models employing methods such as regression, classification, clustering, natural language processing, and forecasting.
• Partner with engineering and platform teams to deploy AI models through reproducible workflows and continuous integration/continuous deployment (CI/CD) pipelines.
• Incorporate model interpretability, fairness, and explainability techniques like SHAP, LIME, and feature importance to promote transparency and accountability.
• Participate in AI governance including Model Risk Management (MRM) to ensure responsible AI utilization and regulatory compliance.
• Design and evaluate A/B testing or controlled experiments to measure model and feature effectiveness.
• Collaborate with Data Product Owners to define outcomes, continuously monitor model performance after deployment, and maintain alignment with business needs.
• Work closely with Data Quality Specialists to guarantee input data complies with quality, lineage, and governance standards.
• Effectively communicate technical results through visual storytelling and presentations tailored for both expert and non-expert stakeholders.

Experience & Qualifications

• More than 6 years of experience in data science, applied machine learning, or advanced analytics roles.
• Proven track record of delivering machine learning models integrated into business workflows or digital products.
• Experienced in agile, multi-disciplinary teams collaborating with Product Owners, Data Analysts, and Data Engineers.
• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related quantitative disciplines.
• Master’s degree or higher in Data Science, Machine Learning, or Applied Statistics is preferred.

Technical Skills

• Expertise in Python and associated libraries including pandas, NumPy, scikit-learn, XGBoost, and LightGBM.
• Strong proficiency in SQL for data extraction, transformation, and quality validation.
• Experience utilizing platforms like Databricks or equivalent data and machine learning environments.
• Knowledge of deep learning frameworks such as PyTorch and TensorFlow.
• Familiarity with machine learning lifecycle and orchestration tools like MLflow, Apache Airflow, Docker, and Kubernetes.
• Understanding of model interpretability methods, AI ethics, fairness, and governance principles.
• Insight into AI governance frameworks, including Model Risk Management documentation and compliance processes.
• Background in cloud computing platforms such as Azure and Snowflake, and using APIs to integrate AI models into applications.
• Proficient with version control systems like Git/GitHub and collaborative coding workflows.

Soft Skills & Attributes

• Excellent communicator able to translate complex technical insights into clear business language.
• Collaborative mindset with curiosity and a strong drive for ongoing learning and technological innovation.

Additional Information

The organization may leverage artificial intelligence tools during the recruitment process to evaluate applications, analyze resumes, and assess candidates' responses for inconsistencies or verification signals. Nonetheless, these tools assist but do not replace the final human judgment in hiring decisions. Candidates seeking further details about data processing during recruitment are encouraged to inquire directly.