Senior/Staff Data Scientist, Analytics

Bluefish; New York (NY); On-site; USD 120,000 - 160,000; Full time

Job summary

Bluefish is seeking a Senior or Staff Data Scientist focusing on Analytics to drive experimentation and causal inference frameworks. This role will have a significant impact on the data science practice, collaborating closely with cross-functional stakeholders from the NYC office following a hybrid working policy.

The ideal candidate has strong SQL and Python skills, a deep statistics background, and extensive experience operating experimentation frameworks at scale. You'll contribute to both internal operations and customer-facing analytics products.

Qualifications

• Strong SQL and Python skills for writing production-quality queries and analytical scripts.
• Deep statistics background including hypothesis testing and causal inference.
• Extensive experience designing and operating experimentation frameworks at scale.

Responsibilities

• Own experimentation end-to-end — design, execute, and analyze A/B tests.
• Drive causal inference work to understand product outcomes.
• Serve as the analytics escalation point for deeper statistical rigor.

Skills

SQL

Python

Statistical analysis

Analytical skills

Communication

Business acumen

Tools

BI/visualization tools

Job description

About the Position

As a Senior or Staff 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)