# Data Scientist
Location: New York, NY (Hybrid) · Employment Type: Full-time · Level: Mid-Senior
[Company] is a product-led growth company building tools that help businesses understand and engage their customers. Our platform serves 15,000+ customers and processes billions of user events monthly.
Our Data Science team doesn't just run reports—we design experiments that shape the product roadmap, build models that power customer-facing features, and partner directly with Product and Engineering to make data-driven decisions at every level.
Why join [Company]?
- Work on real product problems, not just dashboards
- Join a 95-person company with a 12-person data team
- Series B funded ($38M from top-tier investors)
- Competitive compensation with meaningful equity
We're looking for a Data Scientist to join our Product Analytics team. You'll design and analyze experiments, build predictive models, and translate complex data into product decisions. This is a high-impact role where your analyses directly influence what we build next.
The ideal candidate is curious, rigorous, and loves working at the intersection of data and product. You're comfortable running A/B tests, building ML models, and presenting findings to stakeholders—but most importantly, you care about driving outcomes, not just producing analyses.
- Product-focused: You'll work directly with Product and Engineering to answer business questions and validate hypotheses
- Experimentation-heavy: A significant part of your time will involve designing, running, and analyzing A/B tests
- Impact-oriented: Your work will directly shape product features and company strategy
- Technically rigorous: You'll write production-quality Python code and build models that work on real data
- Collaborative: You'll be embedded with product teams, not siloed in a data department
- Not a Data Analyst role: We need someone who can build models and write code, not just create dashboards. Data Analysts focus on reporting and visualization; this role requires statistical modeling and programming.
- Not an ML Engineer role: While you'll build models, you won't be deploying them to production at scale or building ML infrastructure. ML Engineers own the production ML systems; you'll own the analysis and modeling that informs product decisions.
- Not a research position: We value rigor, but we optimize for impact over academic perfection. If you prefer publishing papers to shipping products, this isn't the right fit.
- Design and analyze experiments that drive product decisions
- Build predictive models that improve user experience and business outcomes
- Establish best practices for experimentation and causal inference
- Partner with Product and Engineering to define success metrics for new features
- Communicate insights clearly to stakeholders at all levels
- Design and analyze A/B tests for product features, ensuring statistical rigor and actionable recommendations
- Build predictive models for user behavior (churn prediction, engagement forecasting, segmentation)
- Conduct exploratory analysis to identify growth opportunities and product improvements
- Define and track key metrics for product launches and feature rollouts
- Partner with Product Managers to translate business questions into analytical frameworks
- Present findings to leadership and influence roadmap decisions with data
- Write clean, documented Python code that can be reviewed and maintained
- Mentor junior analysts and contribute to team knowledge sharing
- 4+ years of experience in Data Science, analytics, or a quantitative field
- Strong foundation in statistics: hypothesis testing, regression, causal inference, experimental design
- Proficiency in Python (pandas, NumPy, scikit-learn) for data analysis and modeling
- Expert-level SQL skills for working with large datasets
- Experience designing and analyzing A/B tests and other experiments
- Familiarity with machine learning techniques (classification, regression, clustering)
- Excellent communication skills—ability to explain complex findings to non-technical stakeholders
- Track record of influencing product or business decisions through data
- Experience with Bayesian methods or multi-armed bandits
- Familiarity with causal inference techniques (propensity scoring, difference-in-differences)
- Experience with product analytics tools (Amplitude, Mixpanel, or similar)
- Background in consumer tech, SaaS, or e-commerce
- Experience with visualization tools (Tableau, Looker, or Mode)
- Graduate degree in statistics, economics, or related quantitative field
- Languages: Python (primary), SQL
- Analysis: pandas, NumPy, SciPy, statsmodels
- ML: scikit-learn, XGBoost, LightGBM
- Notebooks: Jupyter, VS Code
- Data Warehouse: Snowflake
- BI/Visualization: Looker, Mode
- Experimentation: Internal platform + Statsig
- Orchestration: Airflow
- Collaboration: GitHub, Notion, Slack
Salary: $140,000 - $185,000 (based on experience and location)
Equity: 0.03% - 0.08% (4-year vest, 1-year cliff)
Benefits:
- Medical, dental, and vision insurance (100% covered for employees)
- Unlimited PTO with 15-day minimum encouraged
- $2,500 annual learning and development budget (conferences, courses, books)
- $1,000 home office setup allowance
- 401(k) with 4% company match
- 16 weeks paid parental leave
- Flexible hybrid work (2-3 days in NYC office)
- Monthly team offsites and annual company retreat
Our interview process typically takes 2-3 weeks. We provide feedback at every stage.
- Step 1: Recruiter Screen (30 min) - We'll discuss your background, career interests, and answer questions about the role and team.
- Step 2: Hiring Manager Intro (45 min) - A conversation with the Data Science Lead about your experience, the types of problems you've worked on, and what you're looking for.
- Step 3: Technical Screen (60 min) - A live analysis exercise where you'll explore a dataset and walk through your thinking. No trick questions—we want to see how you approach problems.
- Step 4: Case Study (90 min) - A deeper product analytics problem. You'll design an experiment, propose metrics, and discuss how you'd analyze results.
- Step 5: Stakeholder Interview (45 min) - Meet with a Product Manager to discuss how you'd collaborate on data-driven decisions.
- Step 6: Team Fit (30 min) - Meet potential teammates and ask any remaining questions.
We aim to make offers within 48 hours of final interviews.
Submit your resume and optionally include a link to your GitHub, portfolio, or any analyses you're proud of. We review every application and respond within 5 business days.
---
*[Company] is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We encourage applications from candidates who may not meet 100% of the qualifications—research shows underrepresented groups are less likely to apply unless they meet every requirement.*
# Data Scientist
**Location:** New York, NY (Hybrid) · **Employment Type:** Full-time · **Level:** Mid-Senior
## About [Company]
[Company] is a product-led growth company building tools that help businesses understand and engage their customers. Our platform serves 15,000+ customers and processes billions of user events monthly.
Our Data Science team doesn't just run reports—we design experiments that shape the product roadmap, build models that power customer-facing features, and partner directly with Product and Engineering to make data-driven decisions at every level.
**Why join [Company]?**
- Work on real product problems, not just dashboards
- Join a 95-person company with a 12-person data team
- Series B funded ($38M from top-tier investors)
- Competitive compensation with meaningful equity
## The Role
We're looking for a Data Scientist to join our Product Analytics team. You'll design and analyze experiments, build predictive models, and translate complex data into product decisions. This is a high-impact role where your analyses directly influence what we build next.
The ideal candidate is curious, rigorous, and loves working at the intersection of data and product. You're comfortable running A/B tests, building ML models, and presenting findings to stakeholders—but most importantly, you care about driving outcomes, not just producing analyses.
## What This Role IS
- **Product-focused:** You'll work directly with Product and Engineering to answer business questions and validate hypotheses
- **Experimentation-heavy:** A significant part of your time will involve designing, running, and analyzing A/B tests
- **Impact-oriented:** Your work will directly shape product features and company strategy
- **Technically rigorous:** You'll write production-quality Python code and build models that work on real data
- **Collaborative:** You'll be embedded with product teams, not siloed in a data department
## What This Role is NOT
- **Not a Data Analyst role:** We need someone who can build models and write code, not just create dashboards. Data Analysts focus on reporting and visualization; this role requires statistical modeling and programming.
- **Not an ML Engineer role:** While you'll build models, you won't be deploying them to production at scale or building ML infrastructure. ML Engineers own the production ML systems; you'll own the analysis and modeling that informs product decisions.
- **Not a research position:** We value rigor, but we optimize for impact over academic perfection. If you prefer publishing papers to shipping products, this isn't the right fit.
## Objectives of This Role
- Design and analyze experiments that drive product decisions
- Build predictive models that improve user experience and business outcomes
- Establish best practices for experimentation and causal inference
- Partner with Product and Engineering to define success metrics for new features
- Communicate insights clearly to stakeholders at all levels
## Responsibilities
- Design and analyze A/B tests for product features, ensuring statistical rigor and actionable recommendations
- Build predictive models for user behavior (churn prediction, engagement forecasting, segmentation)
- Conduct exploratory analysis to identify growth opportunities and product improvements
- Define and track key metrics for product launches and feature rollouts
- Partner with Product Managers to translate business questions into analytical frameworks
- Present findings to leadership and influence roadmap decisions with data
- Write clean, documented Python code that can be reviewed and maintained
- Mentor junior analysts and contribute to team knowledge sharing
## Required Skills and Qualifications
- 4+ years of experience in Data Science, analytics, or a quantitative field
- Strong foundation in statistics: hypothesis testing, regression, causal inference, experimental design
- Proficiency in Python (pandas, NumPy, scikit-learn) for data analysis and modeling
- Expert-level SQL skills for working with large datasets
- Experience designing and analyzing A/B tests and other experiments
- Familiarity with machine learning techniques (classification, regression, clustering)
- Excellent communication skills—ability to explain complex findings to non-technical stakeholders
- Track record of influencing product or business decisions through data
## Preferred Skills and Qualifications
- Experience with Bayesian methods or multi-armed bandits
- Familiarity with causal inference techniques (propensity scoring, difference-in-differences)
- Experience with product analytics tools (Amplitude, Mixpanel, or similar)
- Background in consumer tech, SaaS, or e-commerce
- Experience with visualization tools (Tableau, Looker, or Mode)
- Graduate degree in statistics, economics, or related quantitative field
## Tech Stack
- **Languages:** Python (primary), SQL
- **Analysis:** pandas, NumPy, SciPy, statsmodels
- **ML:** scikit-learn, XGBoost, LightGBM
- **Notebooks:** Jupyter, VS Code
- **Data Warehouse:** Snowflake
- **BI/Visualization:** Looker, Mode
- **Experimentation:** Internal platform + Statsig
- **Orchestration:** Airflow
- **Collaboration:** GitHub, Notion, Slack
## Compensation and Benefits
**Salary:** $140,000 - $185,000 (based on experience and location)
**Equity:** 0.03% - 0.08% (4-year vest, 1-year cliff)
**Benefits:**
- Medical, dental, and vision insurance (100% covered for employees)
- Unlimited PTO with 15-day minimum encouraged
- $2,500 annual learning and development budget (conferences, courses, books)
- $1,000 home office setup allowance
- 401(k) with 4% company match
- 16 weeks paid parental leave
- Flexible hybrid work (2-3 days in NYC office)
- Monthly team offsites and annual company retreat
## Interview Process
Our interview process typically takes 2-3 weeks. We provide feedback at every stage.
- **Step 1: Recruiter Screen** (30 min) - We'll discuss your background, career interests, and answer questions about the role and team.
- **Step 2: Hiring Manager Intro** (45 min) - A conversation with the Data Science Lead about your experience, the types of problems you've worked on, and what you're looking for.
- **Step 3: Technical Screen** (60 min) - A live analysis exercise where you'll explore a dataset and walk through your thinking. No trick questions—we want to see how you approach problems.
- **Step 4: Case Study** (90 min) - A deeper product analytics problem. You'll design an experiment, propose metrics, and discuss how you'd analyze results.
- **Step 5: Stakeholder Interview** (45 min) - Meet with a Product Manager to discuss how you'd collaborate on data-driven decisions.
- **Step 6: Team Fit** (30 min) - Meet potential teammates and ask any remaining questions.
We aim to make offers within 48 hours of final interviews.
## How to Apply
Submit your resume and optionally include a link to your GitHub, portfolio, or any analyses you're proud of. We review every application and respond within 5 business days.
---
*[Company] is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We encourage applications from candidates who may not meet 100% of the qualifications—research shows underrepresented groups are less likely to apply unless they meet every requirement.*