ICONIQ's venture and growth team needs scalable, explainable, and actionable insights to inform decision-making, which requires advanced data science and machine learning applications including statistical modeling, AI frameworks, and predictive analytics applied to investment workflows.
Requirements
- Deep expertise in applied statistics, including optimization, regression, and Monte Carlo methods.
- Proficient in Python and ML libraries such as PyTorch or TensorFlow.
- Experienced with SQL and cloud-based data workflows.
- Familiar with AI/ML frameworks like OpenAI SDK, LangChain, or LlamaIndex.
- Bonus: Experience with graph-based tools (e.g., NetworkX, Neo4j) and survival analysis.
Responsibilities
- Design, validate, and deploy machine learning models to support investment sourcing and portfolio analytics.
- Build explainable predictive scoring models using tools like SHAP or LIME.
- Collaborate with investment teams to develop data solutions that drive high-impact decisions.
- Improve data quality through enrichment, cleaning, and backfilling using statistical methods.
- Apply statistical modeling techniques including regression, clustering, and time-series analysis.
- Build reproducible ML workflows with robust documentation for long-term maintainability.
- Deploy and optimize AI/ML pipelines (e.g., retrieval-augmented generation, vector databases) for research and analysis.
Other
- 7+ years of experience in data science, machine learning, or a related field.
- Strong communication skills with the ability to translate data into actionable insights.
- Background in finance, private equity, or trading environments is a plus.
- Passionate about mentoring others and fostering a collaborative team culture.
- Applicants are expected to work onsite in our New York City or San Francisco office in accordance with our hybrid working policy.
