Senior Data Scientist (New York)

Voleon is a technology company that applies state-of-the-art machine-learning techniques to real-world problems in finance. We have led our industry for over a decade and worked at the frontier of applying machine learning to investment management. We have become a multibillion-dollar asset manager, and we have ambitious goals for the future. Your colleagues will include internationally recognized experts in machine learning research as well as highly experienced technology and finance professionals. The people who shape our company come from other backgrounds, including concert music performance, humanitarian aid, opera singing, sports writing, and BMX racing. You will be part of a team that loves to succeed together. In addition to our enriching and collegial working environment, we offer highly competitive compensation and benefits packages, technology talks by our experts, a beautiful modern office, daily catered lunches, and more.

Founded in 2007 by two leading scientists (see management bios), Voleon supports a culture of curiosity and creativity. We do not silo our teams, and you will enjoy a healthy work-life balance while tackling complex problems in a collegial environment. This role makes a difference: as a machine learning company, data insights are essential to our business.

Over the last few years, the Voleon Group has formed a new team to help advance our data-driven investment initiatives. As a Senior Data Scientist, you will be responsible for harvesting insights from a complex array of data and mentoring the efforts of more junior Data Scientists doing the same. Your role will involve designing systems and directing technical efforts in data curation, analysis, interpretation, and visualization, as well as communicating your findings to members of the research staff and executive leadership. This role is a means to make a difference: as a machine learning company, data insights are essential to our business.

Responsibilities

• Work closely with Technical Product Owners to prioritize high-level needs around analytics and insight extraction, proceeding autonomously with implementation details
• Proactively identify abnormal production behavior, investigate and rationalize this behavior, and/or guiding other Data Scientists to do so
• Communicate results of investigations clearly to relevant stakeholders, with actionable insights clearly highlighted and prioritized
• Design analysis pipelines that automate those analyses found to be valuable for ongoing monitoring, overseeing their implementation, and successful deployment
• Guide and manage the technical efforts of other Data Scientists
• Cultivate in yourself and other Data Scientists some expertise in financial and statistical analysis to gain actionable insights into our production trading and research systems

Requirements

• 3+ years of applied end-to-end industry experience, including internships working with complex datasets, including curation, querying, aggregation, exploratory data analysis, and visualization
• Experience using statistical methods to analyze data, identify patterns, conduct root cause analysis, discover insights, and recommend solutions
• Ability to frame and answer questions quantitatively
• Ability to infer useful forward-looking directions from results of retrospective analysis
• Fluency in managing, processing, and visualizing tabular data using a combination of SQL, Pandas, and R
• Basic software development tooling skills, including bash, Unix, and git
• Ability to refine requirements from ambiguous requests to produce reports demonstrating excellence in communication
• Bachelor’s and Master’s degrees in quantitative disciplines (statistics, biostatistics, data science, computer science, or a related field)

Preferred

• Experience leading the efforts of a team of technical contributors
• Prior industry experience or displayed interest in finance, such as related academic projects, coursework in financial engineering, or industry internships
• Experience developing in a production-facing environment and familiarity with standard concepts and tooling, e.g., CI/CD, git, Airflow