Easter Seals Jobs

• Job Type: Officer of Administration
• Regular/Temporary: Regular
• Hours Per Week: full time
• Salary Range: $140,000-$160,000, Bonus Eligible

Position Summary

Columbia Investment Management Company, LLC, ("IMC") is a wholly owned subsidiary of Columbia University charged with stewarding Columbia's endowment for the current and future support of University operations and with preserving the purchasing power of the endowment over the long-term after inflation. Almost all assets across the $14B+ endowment are managed externally in a diversified strategy that uses active and passive management techniques across a wide range of asset classes. The IMC seeks professionals who can contribute materially to the management of the portfolio. Independent thinking and open dialogue are actively encouraged, with team members contributing to the skills, thinking, qualitative and quantitative analysis related to the overall portfolio, including equities, real estate, other real assets, private equity and a variety of public market strategies. The IMC environment is one in which staff members are expected to develop professionally, work collaboratively, and assume greater responsibilities according to ability and impact.

Columbia IMC is looking to add a Quantitative Analyst to the investment team. The IMC has a long history of using data to support investment decision making and is continually growing and refining its use of data and scientific methods. We are looking for someone who can think independently and work creatively with data. This role is integrated into the investment team, and you will be an active contributor to real and material investment outcomes. We operate a modern data science platform and work primarily in Python in the PyData stack with tabular, time series, and text data. This is a hands-on and very visible position. Because of the responsibilities below, you will be impactful in our collaborative investment deliberations, and you will learn and enhance our unique approach to endowment portfolio management.

This position is for a three-year term starting in July 2025 with an expected end date around July 2028

Responsibilities

• Assist in performing exploratory data analysis, building models, and running experiments to generate data-driven insights across asset classes and geographies which validate or reject our investment hypotheses and refine our assumptions and inputs for all stages of our investment process.
• Contribute to authoring production-grade reports, analytics, and dashboards for the above and for portfolio risk measurement.
• Collaborate on studies and models to encode and automate portions of our investment process.
• Interface with data vendors: participate in the evaluation of the information content of datasets large and small and work with our data engineering team to onboard/ingest/map data.
• Become familiar with our risk management and portfolio construction approach, data science platform, existing datasets, and schemas; provide production support for periodic risk reporting; help maintain in-house proprietary analytics Python packages.

Minimum Qualifications

• Bachelor's degree or equivalent required.
• A minimum of 2-4 years of related experience is required.

Preferred Qualifications

• A Master's degree (or the equivalent combination of education and experience) in a scientific or technical discipline such as Financial Engineering/Mathematical Finance, Statistics, Data Science, Computer Science, or Economics is preferred.

Other Requirements

• Experience in working with time series data in the PyData stack (e.g., pandas, statsmodels, PyMC, scikit-learn). Rigorous exposure to different modeling approaches including classical forecasting, state-space and latent variable models, Bayesian methods, and causal inference.
• Fluency (evidenced by academics, projects, work experience, MOOCs, etc.) in data science foundations including SQL, writing clean code, version control with git & GitLab/GitHub, basic Linux CLI/bash, and performing reproducible research in Jupyter notebooks.
• Experience working with data in the "real world" (e.g., gathering, cleaning, joining raw datasets; creative feature engineering; understanding the assumptions and limitations of common modeling techniques; working with uncertainty in predicted quantities or class membership; presenting to stakeholders with diverse backgrounds) and delivering production-grade "data products".
• Excellent written and verbal communication and presentation skills.
• Basic institutional financial, risk management, research, and/or investment management domain knowledge.