About the Position

As a Data Scientist on the platform prediction team, you will translate our probability of success predictions into measurable portfolio-level outcomes. You will architect core systems that let us rigorously evaluate signals from our AI-driven predictions in public and private equities and our internal portfolio.

This role sits at the intersection of quantitative finance, healthcare data, and AI-driven drug development. If you're excited about applying portfolio construction and risk management fundamentals to one of the most consequential prediction problems in healthcare, this is the role.

Responsibilities

• Work with the team to implement and maintain core portfolio engine: order management system, execution simulation layer, portfolio construction service, and performance tracking
• Design risk frameworks that quantify exposure across a portfolio of drug development bets with radically different risk profiles, timelines, and failure modes
• Run rigorous backtesting experiments with strict temporal constraints to evaluate Formation strategies against baseline approaches and measure marginal signal from new evidence sources
• Coordinate across the organization to integrate internal Formation data sources (clinical trial data, genomic evidence, real-world data) and proprietary tooling into portfolio analytics pipelines
• Work with product and engineering teams to build dashboards and reporting that communicate portfolio performance, risk metrics, and strategy comparisons to both technical and executive stakeholders
• Collaborate with the broader data science team to ensure portfolio-level evaluation feeds back into model improvement and evidence prioritization

About You

We are looking for a highly motivated and experienced Data Scientist to join our team. The ideal candidate will have a strong background in data science, machine learning, and software development, with a proven track record of delivering high-quality results in a fast-paced environment.

Requirements

• MS or PhD in a quantitative field (statistics, finance, physics, computational science, engineering, or related)
• 1-3 years in a quantitative research, data science, or analytics role , finance, healthcare, academic research, or consulting all count; substantive internships qualify
• Strong Python programming skills with experience in data-intensive workflows (pandas, numpy, scipy)
• Solid grasp of core portfolio construction and risk concepts: position sizing, rebalancing, Sharpe ratio, drawdown, volatility, benchmark comparison
• Demonstrated ability to work with messy, real-world datasets , comfortable with data wrangling, deduplication, and quality assessment
• Clear communicator who can present quantitative results to both technical peers and business stakeholders

Preferred Qualifications

• Experience with backtesting frameworks or portfolio simulation (vectorbt, Backtrader, or custom implementations)
• Exposure to healthcare, pharma, or biotech data (clinical trials, claims data, -omics, real-world evidence)
• Familiarity with alternative data in a research or investment context
• Experience with probability-of-success modeling, drug development decision analysis, or health economics
• Comfort with LLMs or AI/ML pipelines in a production or research setting
• Familiarity with dashboard/visualization tools (Streamlit, Plotly, Dash) and pipeline orchestration (Dagster, Airflow)

Total Compensation Range: $154,500 - $202,000

**Compensation Individual compensation is determined by several factors, including role scope, geographic location, and skills & experience. Your offer will reflect where you fall within the range based on these considerations. In addition to base salary, we offer equity, comprehensive benefits, and generous perks. If the posted range doesn't match your expectations, we still encourage you to apply!

**Where We Hire Formation Bio is prioritizing hiring in key hubs, primarily the New York City and Boston metro areas, with a hybrid model requiring 3 days per week in office. Applicants from the Research Triangle (NC) and San Francisco Bay Area may also be considered. Please apply only if you reside in these locations or are willing to relocate.

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You will collaborate closely with our technical teams to support the development and refinement of new AI capabilities, with a particular emphasis on quantitative trading domains.

Your expertise will help select and solve challenging problems in systematic and quantitative strategies , including statistical arbitrage, factor investing, market microstructure modeling, high-frequency / execution algorithms, risk premia harvesting, machine learning-based alpha generation, and portfolio optimisation under realistic constraints.

This role requires strong analytical thinking, rapid adaptation to evolving guidelines, and the ability to provide rigorous, technically sound critiques and solutions in a fast-moving environment.

As a Quantitative Trader, you will directly contribute to xAI's mission by helping train and refine our frontier AI models.

You will teach the models how quantitative traders reason, model markets, evaluate signals, manage risk, and interact with complex financial data and systems.

This involves providing high-quality data in various formats (text, voice, video), writing detailed annotations, critiquing model outputs, recording audio explanations, and occasionally participating in structured video sessions.

We are looking for individuals who are enthusiastic about these data-generation activities, as they form a core part of advancing xAI’s goals in scientific discovery and real-world reasoning.

Quantitative Traders provide labeling, annotation, evaluation, and expert reasoning services across text, voice, and video data modalities to support model training and evaluation.

The role may include recording audio responses, participating in video-based tasks, or producing step-by-step quantitative reasoning traces , all of which are essential job functions required to fulfill xAI’s mission.

All outputs are considered work-for-hire and owned by xAI.

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Your deep domain knowledge will guide the selection and rigorous solving of complex problems in quantitative crypto strategies , including on-chain analysis, DeFi protocols, perpetual futures & derivatives trading, cross-exchange arbitrage, market microstructure in fragmented venues, MEV-aware execution, machine learning for crypto alpha signals, and portfolio/risk management in high-volatility 24/7 markets.

This role demands sharp quantitative thinking, quick adaptation to evolving instructions, and the ability to deliver precise, technically robust critiques and solutions in a dynamic environment.

Responsibilities:

• Utilize proprietary software to deliver accurate labels, rankings, critiques, and in-depth solutions on assigned projects
• Consistently produce high-quality, curated data adhering to rigorous technical and domain standards
• Partner with engineers and researchers to iterate on new training tasks, evaluation frameworks, and crypto-specific benchmarks
• Offer actionable feedback to enhance the efficiency, accuracy, and usability of annotation and data-collection interfaces
• Identify and solve challenging problems from crypto & digital asset domains where you have strong expertise , examples include:

• On-chain metrics analysis and wallet/flow clustering for alpha generation
• DeFi yield farming, liquidity provision, and impermanent loss modeling
• Cross-exchange / CEX-DEX arbitrage and triangular opportunities
• Perpetual futures funding rate strategies and basis trading
• Market microstructure in crypto order books (fragmented liquidity, MEV, sandwich attacks)
• Machine learning models for price prediction, sentiment from social/on-chain, volatility forecasting
• Tokenomics evaluation, airdrop/IDO quantitative assessment, and risk premia in altcoins
• Portfolio optimization and risk management in 24/7 high-volatility environments

• Provide rigorous critiques of model outputs, alternative quantitative approaches, mathematical derivations, code snippets, and step-by-step crypto reasoning
• Efficiently interpret, analyze, and complete tasks based on detailed (and evolving) guidelines

Basic Qualifications:

• Master’s or PhD in a quantitative discipline: Quantitative Finance, Financial Engineering, Computer Science (with crypto/blockchain focus), Statistics, Applied Mathematics, Economics (quantitative), Physics, Operations Research, Data Science, or closely related field or equivalent professional experience as a quantitative crypto trader, systematic strategist, or on-chain analyst
• Superior written and verbal English communication (technical papers, explanatory breakdowns, professional correspondence)
• Extensive hands-on familiarity with crypto data sources and tools (CoinGecko, CoinMarketCap, Dune Analytics, Glassnode, Nansen, Chainalysis, Messari, DefiLlama, The Graph, blockchain explorers, CEX APIs, on-chain datasets, etc.)
• Outstanding analytical skills, attention to detail, and sound judgment under partial information

Preferred Skills and Experience:

• Professional experience in quantitative crypto trading, systematic strategies, or on-chain research at a crypto hedge fund, prop desk, market-making firm, DeFi protocol, or digital asset investment firm
• Publications or public analyses in crypto quant topics (e.g., journals, conferences, reputable blogs, GitHub repos with notable traction)
• Teaching, mentoring, or content-creation experience in crypto/quant finance (university, bootcamps, Twitter threads, newsletters)
• Proficiency in Python for crypto analysis (pandas, NumPy, ccxt, web3.py, etherscan APIs, polars, scikit-learn, PyTorch/TensorFlow for ML models, etc.) and/or Rust/Solidity familiarity
• Experience with backtesting crypto strategies, handling tick-level or on-chain data, managing API rate limits, and dealing with 24/7 market quirks
• Knowledge of MEV, flash loans, oracle manipulation risks, liquidation cascades, or other crypto-native phenomena
• CFA, FRM, CQF, or blockchain-specific certifications (e.g., Certified Blockchain Expert)
• Prior involvement with LLMs, reinforcement learning, or AI evaluation in financial/crypto contexts (strong plus)

Location and Other Expectations:

• Tutor roles may be offered as full-time, part-time, or contractor positions, depending on role needs and candidate fit.
• For contractor positions, hours will vary widely based on project scope and contractor availability, with no fixed commitments required. On average most projects may involve at least 10 hours per week to achieve deliverables effectively though this is not a fixed commitment and depends on the scope of work.
• Tutor roles may be performed remotely from any location worldwide, subject to legal eligibility, time-zone compatibility, and role specific needs.
• For US based candidates, please note we are unable to hire in the states of Wyoming and Illinois at this time.
• We are unable to provide visa sponsorship.
• For those who will be working from a personal device, your computer must be a Chromebook, Mac with MacOS 11.0 or later, or Windows 10 or later.

Compensation and Benefits:

US based candidates: $45/hour - $100/hour depending on factors including relevant experience, skills, education, geographic location, and qualifications. International candidates: $25/hour - $75/hour depending on factors including relevant experience, skills, education, geographic location, and qualifications.

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