Forward Deployed Data Scientist

Forward Deployed Data Scientists are our intelligence layer. You take messy, high-dimensional operational data from portfolio companies and turn it into models that uncover causality, optimize decisions, and forecast outcomes. Where others see noise, you find signal. Your work directly drives the business metrics that matter—cost reduction, revenue lift, operational efficiency.

Each acquisition brings new data challenges. You'll inherit fragmented datasets, undocumented schemas, and business processes that have never been measured rigorously. Your job is to build the statistical and ML foundations that make transformation possible—then codify what worked into reusable frameworks across the portfolio.

This role requires travel. You'll embed with portfolio companies during critical phases—sitting with operations teams, understanding data at the source, and building trust with stakeholders who need to act on your models. Expect 20-40% travel depending on acquisition pace and project needs.

Model Development & Causal Inference

• Design, implement, test, and operationalize machine learning systems that uncover causality, optimize decisions, and forecast outcomes
• Build statistical frameworks and quasi-experimental designs to quantify causal impact of interventions
• Extend classical econometric approaches with modern ML—integrating gradient boosting, deep temporal models, or probabilistic programming where they add value

Data Mastery & Feature Engineering

• Work with large-scale behavioral and operational datasets to extract signal from noise
• Engineer robust features that capture business reality, not just statistical convenience
• Generate actionable insights that translate directly to operational decisions

Production Deployment

• Partner with engineering to deploy ML pipelines that are scalable, maintainable, and observable in production
• Build monitoring and evaluation infrastructure that catches model drift before it impacts business outcomes
• Own the full lifecycle: from exploratory analysis to production system to ongoing improvement

Business Translation & Impact

• Work with product and business stakeholders to translate ambiguous questions into measurable hypotheses
• Deploy models that directly influence business outcomes—not just interesting research
• Communicate technical insights in terms executives can act on

Platform & Playbook Development

• Enhance reproducibility, code efficiency, and team velocity through automation and rigorous software engineering practices
• Identify patterns across portfolio companies that can become reusable modeling frameworks
• Contribute to data science playbooks that reduce time-to-insight for each subsequent acquisition

• Advanced degree (Master's or PhD) in Data Science, Computer Science, Statistics, Econometrics, or a related technical field
• Minimum 3 years of professional experience in data science, with a strong emphasis on applied machine learning, econometrics, or statistical modeling
• Exceptional problem-solving abilities, effective communication, and teamwork skills
• Comfort with ambiguity—you can walk into a newly acquired company with messy data and make order from chaos
• Willingness to travel to portfolio company sites (20-40%) to embed with teams and understand data in context

Core Statistical & ML Craft

• Expertise in causal inference, experimental design, h probabilistic graphical models, and time series analysis
• Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, statsmodels, PyMC, or equivalent)
• A first-principles, data-driven mindset; hands-on and comfortable working with complex, high-dimensional data
• Strong debugging instincts—you can diagnose problems in models and pipelines you didn't build

Production Mindset

• Ship production systems, not just notebooks—you've taken models from exploration to deployed
• Experience with or willingness to learn MLOps and data pipeline orchestration in cloud environments (AWS, GCP, or Azure)
• Understanding of when to use simple models vs. complex ones—interpretability for decisions that need explanation, sophistication where it drives measurable lift

Domain Learning & Systems Thinking

• Ability to rapidly learn new industries—you've gone deep on domains outside your background
• Pattern recognition: you spot what's generalizable vs. one-off across different business contexts
• Experience mapping complex business processes and identifying high-leverage measurement opportunities

Communication & Trust-Building

• Excellent communication skills and the ability to translate technical insights into strategic impact
• Experience working directly with business stakeholders—not just shipping models, but getting people to trust and act on what you build
• Comfort presenting to executives and explaining trade-offs in non-technical terms

Bonus Points

• Experience with or interest in integrating LLMs into ML, probabilistic modeling, or data science workflows
• Background in specific verticals: financial services, healthcare, manufacturing, debt collection
• Experience in early-stage startups, technical consulting roles (MBB), or similar high-ambiguity environments
• Contributions to open-source ML tooling or frameworks

• Repeatable impact: Every acquisition is a new deployment of the frameworks you've built—your work compounds. You will drive millions of EBITDA.
• Platform influence: Your implementations directly shape what becomes the data science stack across the portfolio
• Frontier technology: You're deploying advanced ML capabilities to transform legacy industries that haven't seen rigorous measurement in decades
• Learning velocity: Every portfolio company is a masterclass in a new domain

We encourage you to apply, even if you don't meet all the criteria.