Data Scientist: Investment Research

Key Skills

C Programming, C++, Cloud computing, Data science techniques, Java Programming, Machine learning techniques, MATLAB Programming, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

Two Sigma is a financial sciences company, combining data analysis, invention, and rigorous inquiry to help solve the toughest challenges in investment management, insurance technology, securities, private equity, and venture capital. 

Our team of scientists, technologists, and academics looks beyond the traditional to develop creative solutions to some of the world’s most complex economic problems.

Two Sigma's data engineering platform enables us to harness some of the world's most complex & challenging content, as we structure and integrate new datasets into a diverse ecosystem of syndicated financial and industry-specific data products. Two Sigma's data scientists are focused on joining, enriching, and transforming datasets into novel creative measures of economic activity. We utilize a variety of modeling techniques to generate financially-relevant predictions feeding into Two Sigma's systematic investment management platform, we measure the performance and impact of our predictions in a variety of settings, and we further distill economic insights and thematic analyses from our data & observations.

You will take on the following responsibilities:

• Design & implement granular domain-specific indicators from data, relating them to company, industry, and macroeconomic factors
• Contribute to the design of investing strategies on economic predictions through multiple investing paradigms
• Manage requirements for a set of dependent data products derived from a large portfolio of integrated data feeds
• Contribute to data sourcing strategy across multiple industry data verticals supporting our prediction efforts
• Generate a series of economic insights research reports relating trends in our indicators & predictions to investment themes

You should possess the following qualifications:

• 1-5+ years of experience in applied data analysis & prediction, preferably in an industry setting
• Degree in a technical or quantitative disciplines, like statistics, mathematics, physics, electrical engineering, or computer science
• Demonstrably strong data science modeling intuition and feature engineering creativity
• Expertise in applying statistical techniques for time-series measurement/estimation and prediction
• Intimate familiarity with the potential flaws & fallacies in the applications of specific statistical methods
• Experience specifying & managing requirements for datasets leveraged in your analyses
• Working knowledge of SQL and common data science toolkits : Python, R, Spark, Matlab
• Strong written & verbal communication and presentation skills, with experience crafting a compelling narrative supported by data
• A portfolio of open-data analyses or data-driven research publications would be ideal

You will enjoy the following benefits:

• Core Benefits: Fully paid medical and dental insurance premiums for employees and dependents, competitive 401k match, employer-paid life & disability insurance
• Perks: Onsite gyms with laundry service, wellness activities, casual dress, snacks, game rooms
• Learning: Tuition reimbursement, conference and training sponsorship
• Time Off: Generous vacation and unlimited sick days, competitive paid caregiver leaves
• Hybrid Work Policy: Flexible in-office days with budget for home office setup

The base pay for this role is anticipated to be between $160,000 and $300,000. The anticipated base pay range is based on information as of the time this post was generated. This role may also be eligible for other forms of compensation and benefits, such as a discretionary bonus, health, dental and other wellness plans and 401(k) contributions. Discretionary bonus can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications and experience.