Senior Data Scientist

About the role

Responsibilities

• Conduct in-depth analysis of large datasets to identify trends, patterns, and insights that can inform business strategy.
• Translate complex findings into clear, actionable recommendations.
• End-to-end machine learning delivery, including architecting, training, evaluating, and deploying production-grade predictive models and statistical algorithms across vast, disparate datasets.
• Design and implement sophisticated AI-powered algorithms and predictive models to continuously monitor, analyze, and optimize company performance across all three investment platforms.
• Model governance & monitoring; implement automated monitoring for production models to detect data drift, concept drift, feature skew, and latency degradation. Establish retraining triggers and model registry governance.
• Own the deployment and operationalization of ML models using MLflow, containerization (Docker), and automated CI/CD pipelines. Transition models from notebook prototypes into scalable batch jobs or low-latency REST APIs.
• Continuously refine search parameters, algorithms, and recommendations based on historical deal patterns, dynamic market conditions, and emerging sectors.
• Contribute to the design of scalable data pipelines and data lakes that power advanced analytics and AI applications.
• Champion best practices for data protection, including encryption and access controls, to safeguard sensitive information.
• Collaborate to define and implement robust data quality standards, ensuring all analytical models are built on a foundation of reliable data.
• Operate within our compliance framework to ensure ethical data handling, regulatory compliance, and consistency across the enterprise.
• Work with business stakeholders, data engineers, data stewards, and information architects to ensure data quality and accuracy of analytics and reporting.

Requirements

• Master’s or Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Quantitative Finance, or a closely related quantitative field.
• 5+ years of dedicated experience as a Data Scientist.
• Expert-level Python and strong SQL skills, with experience using NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch, and SQL databases such as PostgreSQL or T-SQL. (R, MATLAB, C++, Java, or C# are a plus.)
• Deep practical expertise with tree-based algorithms and gradient boosting (XGBoost, LightGBM, CatBoost) on structured/tabular business and financial data.
• Strong foundational statistics, including hypothesis testing, regression analysis, regularized models, classification, and time-series forecasting.
• Strong understanding of MLOps solutions for model deployment, management, and scaling (e.g., SageMaker, Vertex AI, Azure ML, MLflow, Docker).
• Proven experience managing the full model lifecycle using MLflow, including experiment tracking, model registry, and artifact storage.
• Wide range of ML algorithms, including k-NN, Naive Bayes, SVM, Decision Forests, Boosting Algorithms, Deep Learning, Time Series Forecasting, NLP, and Reinforcement Learning.
• Ability to work effectively in a dynamic, team-oriented environment.
• Relevant professional certifications such as Chartered Financial Analyst (CFA) or Financial Risk Manager (FRM).
• Strong working knowledge of one of the following: Tableau, Power BI, D3.js, Matplotlib, Seaborn, or ggplot.
• Familiarity with Git-based CI/CD workflows and automated testing for data science repositories.