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.
- EDUCATION/EXPERIENCE REQUIREMENTS: (including certification, licenses, etc.)
- Required:
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.
Preferred
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.
- ABOUT YOU:
- Excellent communication skills to articulate complex data concepts to non-technical stakeholders.
Ability to build strong relationships across departments to ensure collaborative data initiatives.
Pragmatic problem solver; prioritize robust, explainable baseline models (like tuned gradient boosted trees) over unnecessary complexity, scaling up architecture only when the business problem demands it.
Strong analytical and problem-solving skills to innovate and drive improvements in data processes are required.
