Data Scientist (Remote)

7Seventy Recruiting; United States; On-site; USD 138,000 - 220,000; Full time

Benefits offered by this job

Equity offered

Job summary

7Seventy Recruiting seeks a Senior Data Scientist to lead product growth analytics, experimentation, and data-driven decisions across the PLG lifecycle. You will work with Product, Design, Engineering, and Data Engineering to analyze user behavior, onboarding friction, adoption, activation, and retention, using SQL, Python, and ML techniques.

The role emphasizes building analytical frameworks, dashboards, and actionable recommendations while handling small samples and incomplete instrumentation.

Qualifications

• Experience applying product analytics to activation, engagement, retention, or expansion.
• Practical exposure to statistical analysis, experimental design, and quantitative methods.

Responsibilities

• Serve as embedded data science partner to Product, Design, and Engineering to inform strategy and experiment planning.
• Build reusable datasets, dashboards, and data products for cross-functional teams.
• Communicate findings clearly to technical and non-technical stakeholders.

Skills

SQL

Python

Statistics

Experimental design

Causal inference

Cohort analysis

Forecasting

Machine learning

Event data analysis

Data visualization

Tools

Athena

Presto/Trino

BigQuery

Snowflake

Job description

Core Areas:

Product Analytics, Statistical Analysis, Experimental Design, Causal Inference, Predictive Modeling, Machine Learning, SQL, Python

Compensation:

$137,600-$220,000 annually, depending on geographic location, experience, qualifications, and job level. Compensation is structured across three geographic tiers. Equity offered.

About the Role

The Senior Data Scientist will lead product growth analytics and experimentation to support data-driven decisions across the product-led growth (PLG) lifecycle. This position focuses on understanding user behavior, identifying onboarding friction, evaluating product adoption, and measuring the factors that influence activation, conversion, retention, expansion, and long-term customer success. The role combines statistical analysis, experimental design, causal inference, and predictive modeling to inform product strategy and growth initiatives.

Working closely with Product, Design, Engineering, and Data Engineering, this position uses SQL, Python, statistical methods, and machine learning to analyze behavioral and event data. Responsibilities include developing analytical frameworks, evaluating product interventions, building reusable datasets and dashboards, and translating complex findings into actionable recommendations. Analysis must account for small samples, noisy data, incomplete instrumentation, and uncertainty when conventional A/B testing is impractical.

What You'll Do

• Serve as an embedded data science partner to Product, Design, and Engineering, contributing analytical expertise to product strategy, feature proposals, and experiment planning from the earliest stages.
• Develop analytical frameworks covering the complete product-led growth journey, including acquisition, activation, engagement, adoption, expansion, monetization, and long-term durability.
• Conduct funnel analysis, cohort analysis, user journey analysis, and sequence analysis to identify friction points, behavioral patterns, and opportunities to improve product adoption.
• Identify user behaviors and milestones associated with conversion, production readiness, account expansion, churn, and long-term customer success.
• Translate behavioral findings into leading indicators that help identify successful adoption, stalled accounts, and potential growth opportunities.
• Analyze feature discovery, usage, and adoption patterns to recommend product interventions that improve activation, engagement, and expansion.
• Design, execute, and evaluate product experiments using appropriate statistical and causal inference methods.
• Apply matched comparisons, pre/post-intervention analysis, quasi-experimental methods, and other causal techniques when traditional randomized A/B testing is not feasible.
• Work with large, clean datasets as well as smaller or constrained datasets, accounting for limited sample sizes, noisy signals, imperfect comparison groups, and measurement uncertainty.
• Distinguish observed outcomes from assumptions, quantify uncertainty, and communicate the statistical confidence and limitations behind analytical conclusions.
• Evaluate and prioritize product growth opportunities based on expected impact, analytical confidence, implementation effort, and measurement feasibility.
• Build reusable analytical datasets, metrics, dashboards, and tools that reduce repetitive analysis and make product insights accessible to Product, Marketing, Sales, and leadership.
• Collaborate with Engineering and Data Engineering to improve event instrumentation, data quality, and analytical models supporting reliable product measurement.
• Communicate analytical findings through data visualizations, written reports, and practical recommendations for technical and non-technical stakeholders.

Initial Priorities

• Audit existing behavioral signals captured during product onboarding and recommend additional event instrumentation where needed.
• Establish consistent reporting processes and repeatable analytical methods for product-led growth experiments.
• Analyze user behavior following the first successful Activity or Workflow to understand early engagement and adoption patterns.
• Identify behavioral signals associated with production readiness and common stages where users experience friction or stop progressing.
• Map feature adoption journeys across important product areas to uncover opportunities for improved engagement and expansion.

Qualifications

Required Experience

• Experience applying product analytics, behavioral analytics, or event data analysis to improve activation, engagement, retention, conversion, or customer expansion.
• Practical experience with statistical analysis, experimental design, causal reasoning, and quantitative methods used to support product or business decisions.
• Experience analyzing datasets with small sample sizes, noisy signals, incomplete instrumentation, imperfect control groups, or limited opportunities for traditional A/B testing.
• Experience performing cohort analysis, funnel analysis, segmentation, and predictive modeling using supervised or unsupervised machine learning methods.
• Experience developing dashboards and reusable analytical assets using modern business intelligence platforms.
• Experience translating analytical findings into measurable product improvements, growth initiatives, or business actions.
• Experience working with modern data platforms and distributed query engines such as Athena, Presto/Trino, BigQuery, or Snowflake.

Required Skills

• Strong proficiency in SQL and Python for data analysis, statistical modeling, predictive modeling, and analytical automation.
• Solid knowledge of statistics, experimental design, causal inference, uncertainty measurement, and quantitative analytical techniques.
• Ability to select appropriate statistical and experimental methods based on available data, decision requirements, sample limitations, and uncertainty.
• Proficiency in cohort analysis, funnel analysis, user segmentation, behavioral analysis, and product adoption measurement.
• Working knowledge of supervised and unsupervised machine learning methods and their application to predictive and exploratory analysis.
• Ability to rapidly prototype analyses in computational notebooks and transform recurring analytical work into reliable data products, models, or automated workflows.
• Familiarity with cloud infrastructure and object storage technologies, including AWS S3.
• Ability to develop meaningful metrics, analytical dashboards, and data visualizations that support repeatable performance measurement.
• Understanding of event instrumentation, data quality, evolving metric definitions, and analytical limitations caused by incomplete data collection.
• Strong analytical judgment when balancing statistical precision, practical constraints, and the information required to make decisions.
• Ability to identify when additional data collection or analysis could materially change a conclusion and when existing evidence is sufficient.
• Ability to explain statistical methods, uncertainty, experimental findings, and recommendations clearly to technical and non-technical audiences.
• Understanding of developer platforms, cloud infrastructure, usage-based products, and customer adoption patterns for technically complex products.
• Ability to collaborate effectively with Product, Engineering, Marketing, Sales, Finance, and Data stakeholders.