Senior/Staff Data Scientist, Analytics

Crane Venture Partners; New York (NY); Hybrid; USD 100,000 - 140,000; Full time

Job summary

Crane Venture Partners is looking for a Senior Data Scientist focused on Analytics to shape our data science practice. This hybrid role in our NYC office will have you collaborating with cross-functional teams to enhance our data capabilities.

You will be responsible for owning experimentation processes, driving causal analysis, and ensuring the quality of our methodologies.

The ideal candidate has strong SQL and Python skills, a deep statistical background, and the ability to communicate complex findings effectively.

Qualifications

• Strong SQL and Python skills — you write production‑quality queries and analytical scripts.
• Deep statistics background — hypothesis testing, confidence intervals, power analysis, causal inference.
• Extensive experience designing experimentation frameworks at scale.

Responsibilities

• Own experimentation end-to-end — design and analyze A/B tests.
• Drive causal inference work — lead analyses to understand product outcomes.
• Produce internal research papers and contribute to external analyses.

Skills

SQL

Python

Deep statistics background

Data preprocessing and feature engineering

Business acumen

Excellent communication skills

Experience with BI/visualization tools

Tools

Looker

Tableau

Job description

About the Position

As a Senior Data Scientist focusing on Analytics, you'll serve as the statistical backbone of Bluefish’s Data Science team. You'll own experimentation and causal inference frameworks, produce rigorous methodological work, and function as the key escalation point for data-driven decision‑making across the organization.

You'll collaborate closely with Data Analysts, Applied Researchers, and cross‑functional stakeholders — including Product, Marketing Strategy and Services, and Operations — to deliver insights, harden the metrics we report, and deepen the capabilities of our platform.

This role presents a greenfield opportunity to help define Bluefish's data science practice from the ground up, with direct impact on both our internal operations and the analytics products we deliver to customers.

This role is based in our NYC office and follows a hybrid working policy with 3 days in office.

What You'll Be Doing

• Own experimentation end-to-end — design, execute, and analyze A/B tests and other experiments; define statistical significance frameworks
• Drive causal inference work — lead analyses that go beyond correlation to understand the mechanisms behind product and customer outcomes
• Serve as the analytics escalation point — be the go‑to resource across the org when problems require deeper statistical rigor
• Build and maintain methodological standards — document and review statistical methods used across the team; ensure analytical quality and reproducibility
• Produce research — author internal research papers, benchmark studies, and methodology documentation; contribute to external‑facing analyses (e.g., vertical benchmarking, state of AI)
• Support ad‑hoc deep‑dives — respond to data requests from RevOps, MSS, Operations, and leadership with fast turnaround and clear narrative

Qualifications

• Strong SQL and Python skills — you write production‑quality queries and analytical scripts
• Deep statistics background — hypothesis testing, confidence intervals, power analysis, causal inference
• Extensive experience designing and operating experimentation frameworks at scale
• Strong analytical and problem‑solving abilities, with experience in data preprocessing, feature engineering, and model evaluation
• Business acumen — you translate analytical findings into clear, actionable narratives for non‑technical stakeholders
• Excellent communication and narrative crafting skills, with the ability to explain complex methods to product, sales, and executive audiences
• Experience working with LLM or AI product data is a strong plus
• Familiarity with supervised learning techniques (e.g., regression, classification, gradient boosting) for predictive analytics use cases
• Exposure to unsupervised learning methods (e.g., clustering, dimensionality reduction) for customer segmentation or behavioral analysis
• Some experience working alongside or supporting ML model deployment — understanding inference pipelines, feature stores, or model monitoring
• Comfort reading and interpreting NLP/ML research papers to stay current on methodological advances relevant to our data
• Experience with BI/visualization tools (e.g., Looker, Omni, Tableau)