Join SignalFire’s Talent Network for Senior/Staff Data Scientist Roles at VC-Backed Startups

🛑 This is not an application for a specific job. Instead, this is a way to get on the radar of VC-backed startups that are actively hiring Data Science talent. If you have any questions, please direct inquiries to [email protected].

At SignalFire, we partner with top early-stage startups that are shaping the future of technology. Our portfolio spans 200+ innovative companies across AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS.

We’re looking to connect with exceptional Senior and Staff Data Scientists who are excited about using data, experimentation, and machine learning to solve complex product and business problems at high-growth startups.

By joining SignalFire’s Talent Network, your profile will be shared with our portfolio companies, giving you visibility into exclusive early-stage opportunities that may not be publicly listed.

Who Should Join?

We’re looking for data scientists who are:

✔ Passionate about using data to improve products, customer outcomes, and business decisions
✔ Experienced in experimentation, statistical analysis, predictive modeling, or causal inference
✔ Excited to work closely with product, engineering, operations, and business teams
✔ Comfortable operating with incomplete data and ambiguous problems in fast-moving startup environments
✔ Interested in building scalable analytical frameworks, models, and decision-making systems

Typical Roles & Responsibilities

• Partner with product, engineering, and business leaders to identify high-impact opportunities for data science

• Design and analyze experiments to evaluate product changes, growth initiatives, and operational strategies

• Develop predictive, forecasting, recommendation, ranking, or optimization models

• Apply statistical methods and causal inference techniques to measure impact and inform decisions

• Build metrics, analytical frameworks, and dashboards that improve visibility into product and business performance

• Translate complex analyses into clear recommendations for technical and non-technical stakeholders

• Collaborate with engineers to productionize models and integrate data science into customer-facing products

• Identify patterns in user, customer, operational, and market data

• Establish best practices for experimentation, model evaluation, data quality, and analytical rigor

• Mentor other data scientists and raise the technical standard of the broader data organization

• Help shape the company’s data strategy, tooling, and long-term analytical roadmap

Common Qualifications

While each startup has its own hiring criteria, many Senior and Staff Data Scientist roles in our network look for:

• 5+ years of experience in data science, applied statistics, machine learning, decision science, or a related field

• Strong proficiency in Python, R, SQL, or similar analytical languages

• Experience with statistical modeling, experimentation, causal inference, forecasting, or predictive analytics

• Track record of using data to influence product strategy, customer outcomes, or business performance

• Ability to work with large, complex, and imperfect datasets

• Experience partnering closely with product managers, engineers, operators, and executive stakeholders

• Strong communication skills and the ability to explain technical findings clearly

• Experience developing models or analytical systems that are used in production or operational decision-making

• Strong judgment around methodology, measurement, tradeoffs, and uncertainty

• Experience in venture-backed startups or rapidly scaling technology companies may be preferred

• Advanced degree in statistics, economics, computer science, mathematics, operations research, or a related field may be preferred, but is not always required

💡 Technologies You Might Work With:

• Languages & Analysis: Python, R, SQL, pandas, NumPy, SciPy

• Modeling & Machine Learning: scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow

• Experimentation & Statistics: A/B testing, causal inference, Bayesian methods, time-series analysis

• Data Platforms: Snowflake, BigQuery, Redshift, Databricks, Spark

• Visualization & Analytics: Looker, Tableau, Mode, Hex, Amplitude

• Workflow & Development: Jupyter, dbt, Airflow, Git, Docker, cloud platforms

What Happens Next?

1. Submit your application to join SignalFire’s Talent Ecosystem.

2. We review applications on an ongoing basis to identify strong candidates.

3. If there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.

4. No match yet? We’ll keep your profile on file for future Senior and Staff Data Scientist roles across our portfolio.