Data Scientist

Description

The company wants a data scientist to dig into the analytics potential of its many data sources.

You will build and ship production models that check data quality, flag anomalies automatically, set up monitoring, and find new uses for the company's large datasets.

The role suits someone who works well in a small, quick-moving startup, wants to own how complex analytics get done, is happy using agentic workflows, and likes open-ended data problems.

Responsibilities

• Build data systems and machine learning pipelines that backtest across terabyte-scale datasets from very different domains, with confidence estimates.
• Run experiments that reveal bias in incoming data, and pick statistically representative panels for later analysis.
• Work with analysts and applied scientists to improve entity resolution, knowledge graph mapping, and the creation of strong new features.
• Team up with engineering to turn proofs of concept into scalable ML and AI models running in client-facing production.
• Explain complicated results to Product, Engineering, and executive leadership.

Requirements

• A bachelor's degree in data science, computer science, statistics, mathematics, economics, or another quantitative field.
• 2-3 years of professional work in data science or a similar role. Internships and research count.
• Strong Python skills and experience with modern ML frameworks such as sklearn or statsmodels.
• Skill in writing and tuning SQL and building on relational databases.
• Comfort using Git for source control and code review.
• Hands-on experience building models with ML and AI methods, such as supervised and unsupervised classification and regression, or working with LLMs and tabular or time series foundation models.
• Use of AI tools that is responsible and efficient in daily work.
• Enjoy tackling unstructured, novel technical problems.
• Able to work with others and communicate to solve problems.
• Strong spoken and written communication, and the ability to work well with cross-functional partners.
• Experience building dashboards that give non-technical people visibility and control over technical processes, using tools such as Streamlit, Dash, or Tableau.
• Experience with, or interest in, distributed data and compute tools such as dbt, Spark, Ray, or Kubernetes.
• Experience with, or interest in, analytics system design and query engines such as Iceberg, Trino, or StarRocks.
• Experience with cloud services such as AWS or Azure.
• Experience setting up agentic workflows and feedback loops, and using AI development tools.