Senior Data Scientist

Job Description: Senior Data Scientist (8+ Years Experience)

As a Senior Data Scientist, you will lead and execute complex data science projects that drive meaningful business outcomes, working closely with cross-functional teams to design, develop, and implement advanced machine learning models. We are looking for candidates with a proven record of delivering end-to-end ML models on large-scale data — owning the full lifecycle from problem framing and development through to production deployment, post-launch monitoring, and continuous improvement.

Key Responsibilities:

• Lead the design, development, and deployment of advanced ML models (classification, regression, clustering, time series, deep learning) across business verticals.
• Own the full model lifecycle end-to-end — problem framing, feature engineering, model development, production deployment, monitoring, and retraining.
• Productionise multiple models ensuring reliability, scalability, and maintainability; set the standard for how models go live in the organisation.
• Design and oversee post-deployment monitoring frameworks: performance tracking, drift detection, alerting pipelines, and automated retraining strategies.
• Architect and implement scoring and inference pipelines for large-scale data, covering both batch and real-time workflows.
• Utilize Python and SQL with libraries such as NumPy, Pandas, and Scikit-learn; apply deep learning techniques using PyTorch or TensorFlow.
• Work with Snowflake or similar large-scale data platforms for complex data extraction and transformation at scale.
• Define problem scope and translate ambiguous business questions into well-structured data science projects with clear success criteria.
• Mentor junior data scientists, conduct code reviews, and foster a culture of engineering rigour and continuous learning.
• Communicate complex model results, methodology, and business impact clearly to senior stakeholders and leadership.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field.
• 8+ years of professional experience in data science with a strong focus on machine learning, advanced analytics, and ML engineering / MLOps.
• Proven record of working with large-scale data to deliver production-grade ML models with full end-to-end ownership.
• Mandatory: hands-on experience productionising multiple ML models with complete deployment ownership.
• Mandatory: demonstrated post-production monitoring experience — drift detection, performance tracking, and automated retraining pipelines.

Technical Skills:

• Solid understanding of the end-to-end data science lifecycle — from data acquisition, EDA, and feature engineering through to model development, validation, deployment, and post-production monitoring.
• Strong working knowledge of the MLOps lifecycle — including experiment tracking, model versioning, CI/CD for ML, pipeline orchestration, and model governance.
• Proficiency in Python and SQL.
• Deep experience with Snowflake or similar large-scale data tools (e.g. BigQuery, Redshift, Databricks).
• Strong model development expertise with Scikit-learn, XGBoost, PyTorch, or TensorFlow.
• Proven experience productionising ML models — containerisation (Docker/Kubernetes), model serving, API integration.
• Experience architecting scoring and inference pipelines for large-scale batch and real-time data.
• Hands-on experience with post-production monitoring tools: MLflow, Evidently AI, Fiddler, or equivalent.
• Experience with PySpark or equivalent tools for large-scale data processing.
• Strong working knowledge of Git and cloud platforms (AWS, GCP, or Azure).

Nice to Have:

• Experience building propensity models (e.g. churn, upsell, likelihood to purchase) or other marketing-driven model use cases.
• Prior experience or exposure to the payments domain — transaction data, payment behaviour analytics, or related modelling.
• Familiarity with Apache Airflow for orchestrating and scheduling ML pipelines.
• Exposure to tools such as dbt for data transformation, or FastAPI / Flask for model serving and building lightweight ML inference APIs.