Senior Data Scientist

Microsoft

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Redmond, WA
Salary: $119,800–$234,700 / yr
Posted: 3 days ago
Freshness: Confirmed live today
Closes: Apr 21, 2027
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $158k

This role $177k

$94k $250k

below market most similar roles pay here above market

This role pays more than 70% of similar roles. Most pay $126,800–$189,187 — the blue band above. At the midpoint, this role pays about $177k versus about $158k for comparable roles.

Based on 240 similar postings.

Employer

About Microsoft

Microsoft Corporation is a global technology leader producing software, hardware, and cloud services including Windows, Office 365, Azure cloud platform, Xbox gaming, and Surface devices. Industry: Software & Cloud Computing

Microsoft currently has 694 open roles on FindRole.

Listed pay typically runs $119,800–$234,700 across 651 roles with salary data.

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At a glance

TL;DR · Senior Data Scientist

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The Senior Data Scientist will join the team to apply advanced data science techniques to complex customer and business problems. The role involves translating ambiguous business challenges into clear analytical approaches and scalable solutions. Day-to-day responsibilities include developing solutions for specific decision-making scenarios, rapidly prototyping new approaches, and building or operationalizing machine learning solutions in partnership with engineering teams. The candidate will utilize Python, SQL, and large datasets to perform machine learning, statistical analysis, experimentation, causal inference, segmentation, forecasting, and predictive modeling. Notable work includes exploring generative AI, large language models, and AI agents to improve insight generation. The role focuses on creating reusable analytical methods and frameworks while balancing technical rigor with practical business value across areas like personalization, behavioral data, and recommendation systems.

What does a Data Scientist earn in Washington?

Median $212000 from 33 postings across 16 companies.

See salary data

Skills

Python SQL Machine Learning Generative AI Large Language Models LLMs Statistical Analysis Predictive Modeling Causal Inference AI Agents Data Science Experimentation Model Evaluation Production Monitoring Recommendation Systems Segmentation Forecasting

What you'll do

Apply machine learning, statistical analysis, and predictive modeling to solve complex business problems.
Translate ambiguous business challenges into clear analytical approaches and scalable technical solutions.
Explore and implement emerging technologies like generative AI and large language models for insight generation.
Rapidly prototype and evaluate new analytical approaches while balancing technical rigor with business value.
Build and operationalize machine learning solutions in partnership with engineering and technical teams.
Develop reusable analytical methods and frameworks that scale across different business scenarios.
Communicate complex analytical findings and recommendations clearly to both technical and non-technical audiences.
Evaluate solutions based on technical quality, usability, adoption, and real-world business impact.

What we're looking for

Experience applying data science, machine learning, applied statistics, or related quantitative techniques to real-world business problems.
Strong proficiency in Python and SQL with experience working with large, complex datasets.
Strong foundation in machine learning, statistical methods, experimentation, model evaluation, and analytical problem solving.
Experience translating ambiguous business questions into structured analytical approaches and actionable recommendations.
Proven ability to partner with users and stakeholders to understand their needs and tailor solutions to specific scenarios.
Experience taking analytical work beyond analysis or model development into solutions, products, or workflows used by others.
Strong communication skills and the ability to influence across technical and non-technical teams.
Experience applying generative AI, LLMs, AI agents, or other emerging AI technologies to analytics or business workflows (preferred).