Data Scientist [Multiple Positions Available]
About The Position
Duties: Develop, implement and refine quantitative models and algorithms for large-scale portfolio optimization. Design and build scalable software solutions for portfolio analysis and optimization. Perform in-depth analysis to identify opportunities to improve performance of the optimizer. Collaborate with portfolio managers, quantitative researchers, product managers, and other stakeholders to understand investment strategies and translate them into technical requirements. Utilize data science methodologies to extract insights from financial data, identify trends, and inform portfolio construction decisions. Integrate and analyze large datasets from various sources to support model development and validation. Ensure the accuracy, efficiency, and robustness of software solutions through rigorous testing and validation. Address inquiries from daily trading activities and provide immediate solutions or algorithmic trading recommendations. Leverage machine learning and AI techniques to enhance portfolio optimization models and improve predictive accuracy. Lead junior machine learning research analysts in exploring state-of-the-art models and employing them to research projects. Document and communicate technical concepts and findings to both technical and non-technical audiences. Present research outcomes at meetings and conferences. QUALIFICATIONS: Minimum education and experience required: Master's degree in Financial Engineering, Mathematical Finance, Operations Research, or related field of study plus One (1) year of experience in the job offered or as Data Scientist, Research Analyst, Quantitative Analyst, Quantitative Equity Investments, Sales Operations and Strategy Consultant, or related occupation. Skills Required: This position requires experience with the following: implementing quantitative and statistical techniques using Python modules including Pandas, NumPy, SciPy, Seaborn, Scikit-learn, Statsmodels, TensorFlow, PyTorch, and Matplotlib; building portfolio optimization process with Gurobi Optimizer programming; developer tools including Git version control; using Jira to track project progress; financial capital markets and the structure, market behavior, and BARRA risk model for financial instruments including stocks, ETFs, and Mutual Funds; assessing the impact of capital gains tax on investment decisions across all types of investors and applying optimization models to deliver tailored solutions for each scenario; relational database including PostgreSQL; machine learning concepts, including supervised and unsupervised learning, ensemble methods, and time series analysis; feature engineering, model selection, and hyperparameter tuning for machine learning models; application of supervised and unsupervised machine learning models in portfolio management and trading strategies; Ensuring accuracy, efficiency, and robustness of software solutions through testing and validation using python tools including pytest; applying optimization models for tax optimized portfolio construction and direct indexing. Experience in the skills may be gained through graduate-level internships.
Requirements
- Master's degree in Financial Engineering, Mathematical Finance, Operations Research, or related field of study plus One (1) year of experience in the job offered or as Data Scientist, Research Analyst, Quantitative Analyst, Quantitative Equity Investments, Sales Operations and Strategy Consultant, or related occupation.
- Experience with implementing quantitative and statistical techniques using Python modules including Pandas, NumPy, SciPy, Seaborn, Scikit-learn, Statsmodels, TensorFlow, PyTorch, and Matplotlib
- Experience with building portfolio optimization process with Gurobi Optimizer programming
- Experience with developer tools including Git version control
- Experience with using Jira to track project progress
- Experience with financial capital markets and the structure, market behavior, and BARRA risk model for financial instruments including stocks, ETFs, and Mutual Funds
- Experience with assessing the impact of capital gains tax on investment decisions across all types of investors and applying optimization models to deliver tailored solutions for each scenario
- Experience with relational database including PostgreSQL
- Experience with machine learning concepts, including supervised and unsupervised learning, ensemble methods, and time series analysis
- Experience with feature engineering, model selection, and hyperparameter tuning for machine learning models
- Experience with application of supervised and unsupervised machine learning models in portfolio management and trading strategies
- Experience with ensuring accuracy, efficiency, and robustness of software solutions through testing and validation using python tools including pytest
- Experience with applying optimization models for tax optimized portfolio construction and direct indexing
Responsibilities
- Develop, implement and refine quantitative models and algorithms for large-scale portfolio optimization.
- Design and build scalable software solutions for portfolio analysis and optimization.
- Perform in-depth analysis to identify opportunities to improve performance of the optimizer.
- Collaborate with portfolio managers, quantitative researchers, product managers, and other stakeholders to understand investment strategies and translate them into technical requirements.
- Utilize data science methodologies to extract insights from financial data, identify trends, and inform portfolio construction decisions.
- Integrate and analyze large datasets from various sources to support model development and validation.
- Ensure the accuracy, efficiency, and robustness of software solutions through rigorous testing and validation.
- Address inquiries from daily trading activities and provide immediate solutions or algorithmic trading recommendations.
- Leverage machine learning and AI techniques to enhance portfolio optimization models and improve predictive accuracy.
- Lead junior machine learning research analysts in exploring state-of-the-art models and employing them to research projects.
- Document and communicate technical concepts and findings to both technical and non-technical audiences.
- Present research outcomes at meetings and conferences.
Benefits
- competitive total rewards package including base salary determined based on the role, experience, skill set and location
- Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions
- comprehensive health care coverage
- on-site health and wellness centers
- a retirement savings plan
- backup childcare
- tuition reimbursement
- mental health support
- financial coaching
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Number of Employees
5,001-10,000 employees
