5,634 Data Scientist Jobs (October 2026) - Apply with AI
Explore data scientist jobs across roles that may involve modeling, experimentation, analytics, machine learning, data storytelling, and work with product or business teams. Listings can vary by seniority, workplace setup, and technical stack, so review each data scientist role for the tools, domain focus, and collaboration expectations. Create an account to explore the full job feed and auto-apply with LiftmyCV AI Agent.
NLP Data Scientist
HybridSteerBridge is a modern technology company delivering innovative, mission‑focused solutions to the U.S. Government and private sector. Leveraging deep expertise in federal acquisition, digital transformation, and emerging technologies, we deliver…
Posted 1 week ago
NLP / LLM Data Scientist
RemoteDandelion Health is seeking a skilled NLP / LLM Data Scientist to leverage clinical and text-based data for healthcare improvements. This role involves building and utilizing large language models and ML-based approaches for data abstraction…
Posted 1 week ago
Junior NLP Data Scientist
On-siteStaff - Non Union Job Category M P - AAPS Job Profile AAPS Salaried - Information Systems and Technology, Level A Job Title Junior NLP Data Scientist Department Nunez Laboratory | Department of Psychiatry | Faculty of Medicine Compensation…
Posted 4 days ago
AI and ML Data Scientist
On-siteAI and ML Data Scientist The Opportunity: As an Agentic AI Engineer and Data Scientist for military intelligence, you’re excited by the opportunity to design, develop, and deploy advanced AI systems that help analysts transform complex,…
Posted 1 week ago
AI/ML Data Scientist
On-siteAs an AI/ML Data Scientist at Guidehouse, you will join a dynamic team dedicated to enhancing data value and automating processes for clients, specifically within the Department of Homeland Security. You will design and implement AI solutions…
Posted 2 weeks ago
Applied Data Scientist
On-siteThe City of Baltimore is seeking an Applied Data Scientist to join the Department of Planning's Data and Performance team. The ideal candidate will be responsible for integrating data from various systems, developing reliable pipelines, creating…
Posted 3 weeks ago
Growth Data Scientist
ANYViktor is seeking a Growth Data Scientist to enhance customer lifetime value models. This role involves using predictive analytics to guide acquisition, pricing, and sales investments. Candidates will work on retention and expansion metrics while…
Posted 2 weeks ago
Senior Statistical Data Scientist
On-siteThe role is/includes An Individual Contributor role Productive hands on programming, supporting deliverables in the study/project/portfolio/standards team, of medium – high complex statistical programming deliverables to support assets…
Posted 1 week ago
Applied Research - Data Scientist
On-siteApplied Research - Data Scientist Description - Job Summary This role is responsible for conducting innovative research focused on machine learning (ML) and natural language processing (NLP), translating insights into experimentation and…
Posted 4 weeks ago
Principal Data Scientist (NLP + Applied AI)
RemoteWiley is seeking a Principal Data Scientist with expertise in NLP and Applied AI to join a small team responsible for building systems that transform scientific literature into actionable research intelligence. The successful candidate will own content…
Posted 2 weeks ago
Research Data Scientist
RemoteInnodata Inc. is seeking a Research Data Scientist specializing in Generative AI and Large Language Models (LLMs) to join their AI/LLM Delivery Unit. The ideal candidate will possess strong research and analytical capabilities, hands-on AI/ML expertise,…
Posted 2 weeks ago
Research Data Scientist
HybridAcademic Level A ($89,579 – $120,621) or Academic Level B ($127,138 - $150,425) plus 17% superannuation and annual leave loading Full-time, 3-year fixed term appointment Macquarie University, Wallumattagal Campus, North Ryde Turn complex…
Posted 3 weeks ago
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Data Scientist Salary Data in the United States (October 2026)
Review salary information for Data Scientist roles across the United States, based on 5,634+ active postings. Use this section to compare compensation signals for data science openings that may include analytics, modeling, machine learning, and applied statistics work.
Average Salary
$110k
$151k
$190k
25th
50th
75th
Based on 5,634 roles currently tracked by LiftmyCV. Last updated on Sep 2, 2026
Salary Distribution
Based on 5,634 roles currently tracked by LiftmyCV. Last updated on Sep 2, 2026
| Experience Level | 25th Percentile | Median (50th) | 75th Percentile | Sample Size |
|---|---|---|---|---|
| Overall | $110,000 | $151,000 | $190,000 | 5,634 |
| Entry-Level | $89,437.5 | $120,226.25 | $163,100 | 36 |
| Mid-Level | $100,750 | $145,590 | $175,625 | 96 |
| Senior-Level | $130,900 | $173,200 | $207,500 | 117 |
"For Data Scientist jobs in 2026, I’m seeing employers separate research-heavy profiles from product analytics and machine learning work much earlier in the process. A resume that only says Python, SQL, and modeling can feel too broad. The sharper applications connect experimentation, stakeholder questions, model evaluation, and business decisions, especially when the role sits between analytics, engineering, and product teams."
Marina's Market Take
Senior HR Leader & Lead Tech Recruiter
How to Land a Data Scientist Job in 2026
Data scientist jobs in 2026 usually reward candidates who can connect modeling work to business decisions, not just list Python libraries or coursework. Your application should show where you have worked with messy data, chosen a modeling approach, evaluated results, and explained the tradeoff to a product, analytics, operations, finance, or growth team.
Position yourself within a clear data scientist lane. If your background is product analytics, emphasize experimentation, metrics design, user behavior analysis, and SQL-heavy work. If you are closer to machine learning, show model development, feature engineering, validation, deployment awareness, and monitoring. For research-oriented data science roles, highlight statistical methods, causal inference, forecasting, NLP, computer vision, or other methods that match the listing. A general “data scientist” profile can feel unfocused when the role description is asking for a product decision partner or a modeling specialist.
- Lead with tools only when they support the work. Python, R, SQL, pandas, scikit-learn, Spark, notebooks, dashboards, and cloud platforms should appear next to projects, datasets, or production decisions.
- Show business context. Mention churn, pricing, fraud, recommendations, experimentation, forecasting, marketplace efficiency, customer segmentation, or operational planning when those problems match the job description.
- Make your portfolio selective. A few finished projects with clear assumptions, evaluation metrics, and limitations are more useful than a long list of disconnected notebooks.
- Read seniority signals carefully. Entry-level data scientist roles may focus on SQL, analysis, and supervised modeling, while senior roles often expect project ownership, stakeholder communication, ambiguous problem framing, and mentoring.
- Search by adjacent titles. Data scientist openings may also appear as machine learning scientist, product data scientist, decision scientist, applied scientist, statistical analyst, or analytics scientist.
LiftmyCV helps you find data scientist jobs that match your skills, experience, and preferred work style, then auto-apply to relevant roles faster.
Required Skills
Resume Tips
For data scientist resumes, lead with the work that shows how you turned data into decisions: predictive modeling, experimentation, forecasting, segmentation, NLP, recommendation systems, anomaly detection, or causal analysis. Name the tools employers expect to scan quickly, such as Python, SQL, R, pandas, scikit-learn, TensorFlow, PyTorch, Spark, Databricks, Snowflake, Tableau, Looker, or Airflow. If you have an MS, PhD, AWS, Google Cloud, Azure, or Databricks certification, place it where it supports the role, not as filler.
Cut long coursework lists, vague “data-driven” claims, and project descriptions that never explain the business question. A Kaggle model can help, but only if you explain the dataset, evaluation metric, and why the approach mattered. For 2026 data scientist jobs, hiring managers usually need to see clean evidence of modeling judgment, statistical reasoning, and communication with product, marketing, risk, finance, or operations teams.
Weak bullet: “Built machine learning models to analyze customer data.”
Stronger bullet: “Built a Python and SQL churn model using XGBoost, validated performance with AUC and lift charts, and translated risk segments into retention recommendations for the lifecycle marketing team.”
Use bullets that pair methods with outcomes: A/B testing tied to product changes, forecasting tied to inventory planning, or clustering tied to campaign strategy. LiftmyCV helps you create an ATS-friendly data scientist resume tailored to each job, so your skills and experience better match what employers are looking for.
How to Prepare for Interviews
Interviews for Data Scientist jobs in 2026 usually test both statistical judgment and the ability to turn messy data into a usable decision. Prepare examples that show how you framed a problem, selected a model or analysis method, checked assumptions, and explained tradeoffs to product, operations, or business stakeholders.
Expect technical screens with SQL, Python, statistics, and experimentation. A common format is a live query exercise, such as finding cohort retention from event tables, followed by questions about joins, missing data, outliers, and metric definitions. You may also see a take-home case asking you to analyze churn, forecast demand, or evaluate an A/B test with clear recommendations.
Build a short portfolio review around two or three projects with metrics, not just notebooks. Be ready to discuss feature selection, leakage, validation strategy, model performance, and why a simpler baseline may have beaten a complex approach. For product-facing Data Scientist roles, practice explaining confidence intervals, experiment design, and business impact without hiding behind jargon.