> Markdown version of [/jobs/ext/3034813-data-scientist](https://www.wearedevelopers.com/jobs/ext/3034813-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Scientist

- **Company:** The Boulevard
- **Location:** New York, NY, United States (Remote available)
- **Experience:** Expert
- **Salary:** $164,000.0 - $205,000.0
- **Contract:** Franchise
- **Skills:** Artificial Intelligence, Amazon Web Services, Business Analytics Applications, Data Analysis, Information Engineering, Python (Programming Language), Machine Learning, Mixpanel, Salesforce.Com, SQL Databases, Tableau (Software), Value Engineering, Usage Analysis, Jupyter Notebook, Snowflake, Build Process, Optimizely, Looker Analytics, Software Version Control, Data Pipelines
- **Published:** September 23, 2026
- **Apply:** https://www.builtincolorado.com/job/staff-data-scientist/11321987?handler=ApplyRedirect

## About the Role

* 8+ years of proven experience in data science or product analytics or engineering in a B2B SaaS or high-growth technology environment, with meaningful time spent in early-stage or low data-maturity environments - you've built the foundation, not just worked on top of one someone else laid. Act Like an Owner
 * Fluency with data analysis and BI tools: SQL, analytical tools like Python / Jupyter notebooks, Snowflake, DBT, Sigma for data and reporting pipelines and AWS infrastructure to productionalize analytics / models in a repeatable way; strong proficiency with data modeling. Know Your Sh*t
 * Direct experience designing and executing product instrumentation strategies - defining event schemas, authoring tracking plans, and ensuring reliable data capture in partnership with product and engineering
 * Expertise in building dashboards and visualizations using platforms such as Sigma, Looker, Tableau, or similar - with a track record of creating self-serve tools that teams actually use
 * Significant experience working directly with product managers and leaders - translating data findings into actionable opportunities and tradeoffs that drive strategy and roadmap investment
 * Demonstrated ability to design and execute deep-dive analyses across the full product lifecycle - including funnel diagnostics, cohort and retention modeling, and behavioral segmentation - translating statistical findings into clear, decision-ready narratives for product and leadership audiences
 * Deep, hands-on statistical and machine learning expertise applied to customer behavior. Regression and classification, propensity and churn-risk modeling, clustering and behavioral segmentation, survival and time-to-value analysis - with the judgment to reach for the simplest method that answers the question, validate it honestly, and communicate uncertainty as clearly as the estimate. Know Your Sh*t
 * Proven experience owning experimentation end to end, designing tests before a feature ships (hypothesis, primary and guardrail metrics, randomization unit, power and duration), running the analysis, and delivering a defensible read on impact. Hands-on experience on a platform such as Statsig, Optimizely. Equally important: the causal-inference toolkit and the judgment to use it when a clean A/B test isn't possible. Know Your Sh*t
 * Ability to build and own your own data pipelines. Production-grade DBT models, transformations, and orchestration in Snowflake, written in code with tests, documentation, and version control. You're self-sufficient from raw event to analysis-ready asset, and you partner with data engineering on platform and scale rather than waiting in their queue. Act Like an Owner
 * Clear, confident communication with stakeholders at any level - you can build a narrative that lands with a PM or the executive team, and you deliver it with the kind of presence that builds trust. Show Up With Style
 * High level of ownership with a demonstrated ability to manage projects end-to-end, identify opportunities, navigate ambiguity, and build processes that scale - comfortable thriving in fast-paced, dynamic environments with multiple competing priorities. Make an Impact
 * Proven track record of partnering cross-functionally and using product data to influence leadership decisions and outcomes - whether shaping go-to-market strategy, informing customer success priorities, or driving alignment across teams; you earn trust by being direct, generous with knowledge, and consistent in how you show up

Preferred

 * Experience evaluating or implementing product analytics tooling such as Amplitude, Mixpanel, or similar platforms, Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independent analytical problem-solving, SQL or Python proficiency, strong communication, and ownership of ambiguous initiatives through measurable business impact.

## Description

* Build Boulevard's product data foundation - partnering across the Product Development organization to define what needs to be captured and how, and designing the models and the tech stack that translate raw data into clean, reliable and scalable analysis-ready assets in partnership with data engineering
 * In tight partnership with Product, develop data-driven recommendations that inform strategy and drive action - through engaging narratives, effective data storytelling, and visualizations adapted to the audience, from individual contributors to executive leadership
 * Build scalable, intuitive and self-serve dashboards that empower teams and stakeholders to independently explore data and make informed strategic decisions; fostering a data-driven culture by educating and enabling stakeholders to interpret data and act on it with confidence. Operationalize product analytics. Connect product analytics to OKRs and business outcomes.
 * Own deep-dive and exploratory analyses that up-level understanding of customers and their relationship with the product (e.g. funnel analysis, retention curves, cohort behavior, feature adoption) - surface insights proactively and build analytical narratives that support strategic business cases and influence product direction
 * Be the bridge between product data and the broader organization - ensuring insights actively inform and influence cross-functional decisions and outcomes
 * Create team processes and analytical workflows that enforce data accuracy and scale as the function grows; advocate for the tooling investments the team requires
 * Experimentation, design, not just readout. Own the experimentation practice for Product Development - partner with PMs and engineering to design experiments before feature releases (hypothesis, primary and guardrail metrics, unit of randomization, power and duration), then run the analysis and deliver a clear, defensible read on impact. Establish the standards, templates, and Statsig/tooling workflows that make experimentation the default way Boulevard evaluates a feature launch, and be honest about when a clean test isn't possible - designing the best available quasi-experimental read (pilot cohorts, staged rollouts, difference-in-differences, pre/post with controls) instead.
 * Modeling customer behavior. Apply statistical and machine learning methods to explain and predict customer behavior - propensity and adoption models, retention and churn risk, segmentation and clustering of usage patterns, time-to-value and activation modeling, and driver analysis that separates correlation from cause. Choose the simplest method that answers the question, validate rigorously (holdouts, backtesting, recall/precision trade-offs framed by business cost), and communicate uncertainty as clearly as the point estimate.
 * Get model output into the workflow. Take models from analysis to production - partner with data engineering to schedule, monitor, and version them, and land the output where it drives action (in-product surfaces, Gainsight, Salesforce, CSM and PM workflows). Own model performance over time, including drift, retraining, and retiring models that stop earning their keep., Leads product measurement and analytical data-product development across compensation and workforce datasets. Owns metrics, event instrumentation, experimentation, dashboards, data quality, validation, and privacy standards. Partners cross-functionally with Product, Engineering, Data Science, Legal, Privacy, Sales, and Customer Success to translate complex usage and business data into customer-facing insights and product decisions. Provides senior analytical leadership for ambiguous initiatives from discovery through production, commercialization, and launch.
Top Skills: Ai-Assisted Development ToolsAmplitudeBigQueryGenerative AiLookerMixpanelPythonRSQLTableau
Jerry


## Related Videos

- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [Quantum computing for developers: Solving optimization problems with Qiskit](https://www.wearedevelopers.com/videos/777-quantum-computing-for-developers-solving-optimization-problems-with-qiskit)
- [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team)
- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated)
- [Hacking AI at the Edge of the Indian Ocean](https://www.wearedevelopers.com/videos/100177-hacking-ai-at-the-edge-of-the-indian-ocean)

## Related Articles

- [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)
- [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again)