Senior Data Scientist

• 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.

Required:

• Masters or Bachelors 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 Matplotlib Seaborn or ggplot.
• Familiarity with Git-based CI/CD workflows and automated testing for data science repositories.

• 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.