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Applied Data Scientist Jobs (NOW HIRING)

GAINSystems

Applied Data Scientist

Atlanta, GA · On-site

Applied Data Scientist Applied Research Group - Supply Chain Optimization About GAINS GAINS is on a mission to make supply chains smarter, faster, and self-improving, powered by AI. Our decision ...

GAINSystems

Applied Data Scientist

Atlanta, GA · On-site

Applied Data Scientist Applied Research Group - Supply Chain Optimization About GAINS GAINS is on a mission to make supply chains smarter, faster, and self-improving, powered by AI. Our decision ...

Hop

Applied Data Scientist

San Francisco, CA

What we require BS/MS in Statistics, Computer Science, Applied Mathematics, or a quantitative field. 3-5 years of applied data science; minimum 2 years working with NLP or large-scale text data in ...

Eliassen Group

Senior Applied Data Scientist

Almont, CO · Remote

$80 - $95/hr

Design, develop, and own applied data science and optimization models supporting a pricing engine for products and services Apply prescriptive analytics, forecasting, and operations research ...

UHS

Applied Data Scientist

King Of Prussia, PA · On-site

The Applied Data Scientist recommends and implements methodologies appropriate to large scale data analytics, including those commonly involved in large-scale Machine Learning (ML) and Artificial ...

Hop

Applied Data Scientist

San Francisco, CA

What we require • BS/MS in Statistics, Computer Science, Applied Mathematics, or a quantitative field. • 3-5 years of applied data science; minimum 2 years working with NLP or large-scale text ...

UHS

Applied Data Scientist

King Of Prussia, PA · On-site

The Applied Data Scientist r ecommends and implements methodologies appropriate to large scale data analytics, including those commonly involved in large-scale Machine Learning (ML) and Artificial ...

Lorven Technologies

Senior Applied Data Scientist

Senior Applied Data Scientist / Technical Problem Solver Location : Remote Fulltime Summary: Hands-on, independent contributor who can originate work, turn ambiguous asks into crisp, testable ...

Cresta

Applied Data Scientist

OR · On-site +1

$150K - $200K/yr

As an Applied Data Scientist on our Insights team, you will help pioneer the next generation of Cresta features that power how enterprises understand and optimize every customer interaction. You'll ...

3Play Media

Senior Applied Data Scientist

Boston, MA · On-site +1

$150K - $180K/yr

The Role We are looking to grow our core Applied Science team by adding a "Senior Applied Data Scientist". This is an individual contributor role on a highly collaborative team. The Applied Science ...

New

Booz Allen Hamilton

Applied Data Scientist

Arlington, VA · On-site

$99K - $225K/yr

Applied Data Scientist The Opportunity: As a data scientist, you're excited about building intelligent systems, not just analyzing data. You're motivated to design, develop, and deploy machine ...

Cresta

Applied Data Scientist

As an Applied Data Scientist on our Insights team, you will help pioneer the next generation of Cresta features that power how enterprises understand and optimize every customer interaction. You'll ...

Microsoft

Senior Applied Data Scientist

Redmond, WA · On-site

$158K - $258K/yr

Overview The Ads Data Management Platform team is seeking Applied Scientists to elevate the quality of User Identity graph as it scales across all MAI surfaces including copilot driving measurable ...

Microsoft AI

Senior Applied Data Scientist

Redmond, WA · On-site

Microsoft AI is seeking a highly skilled and experienced Applied Data Scientist to join their dynamic team. The role focuses on elevating the quality of User Identity graph and involves designing and ...

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Applied Data Scientist information

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How much do applied data scientist jobs pay per year?

As of Jun 10, 2026, the average yearly pay for applied data scientist in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What are applied data scientists?

Applied data scientists are professionals who use statistical analysis, machine learning, and programming to extract insights from data and solve real-world business or research problems. Unlike theoretical data scientists, applied data scientists focus on implementing practical solutions that directly impact organizations, such as improving processes, predicting trends, or optimizing operations. They work closely with stakeholders to understand requirements, develop predictive models, and communicate results in a clear and actionable way. Their expertise typically spans data wrangling, model building, and deployment of data-driven applications.

What are the key skills and qualifications needed to thrive as an Applied Data Scientist, and why are they important?

To thrive as an Applied Data Scientist, you need a strong background in statistics, machine learning, and programming languages such as Python or R, often supported by a degree in a quantitative field. Familiarity with tools like TensorFlow, PyTorch, SQL, and data visualization platforms, as well as experience deploying models in production, is typically required. Critical thinking, effective communication, and problem-solving skills help translate complex data insights into actionable business solutions. These competencies are essential for extracting value from data and driving impactful, data-informed decisions within organizations.

What is the difference between Applied Data Scientist vs Data Analyst?

AspectApplied Data ScientistData Analyst
Required SkillsStatistical modeling, machine learning, programming (Python, R)Data visualization, basic statistics, Excel, SQL
Work EnvironmentDeveloping predictive models, advanced analytics, researchReporting, data cleaning, descriptive analysis
Common ToolsPython, R, SQL, cloud platformsExcel, Tableau, Power BI, SQL
Industry UsageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Applied Data Scientists focus on building predictive models and advanced analytics, requiring programming and statistical skills. Data Analysts primarily handle data reporting and visualization, with a focus on descriptive insights. Both roles are essential in data-driven organizations but differ in complexity and scope.

What is the 80 20 rule in data science?

In data science, the 80/20 rule, also known as the Pareto principle, suggests that roughly 80% of results come from 20% of the efforts or features. Applied by applied data scientists, it helps prioritize data cleaning, feature selection, and model tuning to focus on the most impactful variables and tasks.

What are the typical collaboration dynamics between Applied Data Scientists and other teams within an organization?

Applied Data Scientists frequently work cross-functionally, partnering with engineering, product management, and business analytics teams. They translate complex data insights into actionable recommendations, often presenting findings to non-technical stakeholders. Regular interaction with software engineers is common for model deployment, while close alignment with business teams ensures data solutions address real-world challenges. Effective communication and adaptability are key to thriving in these collaborative environments.
More about Applied Data Scientist jobs
What cities are hiring for Applied Data Scientist jobs? Cities with the most Applied Data Scientist job openings:
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What job categories do people searching Applied Data Scientist jobs look for? The top searched job categories for Applied Data Scientist jobs are:
Applied Data Scientist jobs near you
Infographic showing various Applied Data Scientist job openings in the United States as of June 2026, with employment types broken down into 2% Internship, 2% As Needed, 86% Full Time, 5% Part Time, and 5% Contract. Highlights an 90% Physical, 3% Hybrid, and 7% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.
Applied Data Scientist

Applied Data Scientist

GAINSystems

Atlanta, GA • On-site

Full-time

Posted 9 days ago

Job description

Applied Data Scientist
Applied Research Group - Supply Chain Optimization
About GAINS
GAINS is on a mission to make supply chains smarter, faster, and self-improving, powered by AI. Our decision intelligence platform doesn't just support decisions, it drives them by aligning strategy, planning, and execution across every level of the supply chain. We serve inventory-intensive industries where the stakes are high and the complexity is real, helping customers move from reactive, spreadsheet-driven planning to continuously learning, AI-led operations that deliver measurable results fast. At GAINS, we call it Moving Forward Faster- and it's not a tagline, it's how we're redefining what's possible in driving supply chain decisions.
About the Role
As an Applied Data Scientist on the Applied Research Group at GAINS, you will research, design, build, and deploy production ML models that directly improve supply chain outcomes for enterprise customers. This is a hybrid role that spans the full ML lifecycle-from exploratory analysis and model development through production deployment and ongoing performance tuning. Your work will address core supply chain problems where machine learning delivers measurable business value.
On any given week, you might be designing a new feature engineering approach, running experiments to evaluate alternative modeling techniques, debugging model drift for a specific customer, or building pipeline infrastructure to operationalize a new ML capability. You will collaborate closely with product managers, professional services, software engineers, and customer-facing teams to translate complex supply chain challenges into well-scoped ML solutions.
This is a hands-on IC role with high autonomy and direct impact on customer outcomes and revenue. You will own ML projects end-to-end-the science and the engineering.
A Day in the Life
  • Research, design, and develop machine learning models for supply chain applications that drive measurable improvements in operational efficiency and planning accuracy
  • Perform exploratory data analysis, statistical modeling, and feature engineering on large, complex supply chain datasets to identify signals and improve model performance
  • Design and run experiments to evaluate new modeling approaches, loss functions, feature sets, and hyperparameter configurations-interpreting results and translating findings into production improvements
  • Build and maintain robust ML pipelines that process, clean, and transform data from enterprise supply chain systems (SQL databases, APIs, ERP integrations)
  • Deploy and maintain models in cloud-based production environments, managing the full lifecycle from training through inference and monitoring
  • Implement model evaluation, drift detection, and monitoring frameworks to ensure reliability across diverse customer environments
  • Diagnose and resolve model performance issues for individual customers-investigating data quality, feature behavior, and distributional shifts
  • Partner with product managers, professional services, and engineering teams to understand customer problems and scope ML solutions appropriately
  • Communicate findings, model behavior, trade-offs, and recommendations clearly to both technical and non-technical stakeholders
  • Contribute to the team's technical direction on ML methodology, architecture, tooling, and best practices

Required Qualifications
  • Bachelor's degree in Computer Science, Statistics, Data Science, Engineering, Operations Research, or a related technical field; or equivalent professional experience
  • 3+ years hands-on experience in applied machine learning or data science roles, with models developed and deployed to production
  • Strong Python skills with experience writing clean, maintainable, production-grade ML code
  • 3+ years professional SQL experience, including complex queries against large enterprise datasets
  • Deep understanding of statistical and machine learning methods: gradient boosting (LightGBM, XGBoost, CatBoost), regression, decision trees, clustering, time series techniques, and model evaluation methodology
  • Experience with feature engineering for structured and tabular data, including domain-informed feature design, temporal feature construction, and feature selection techniques
  • Demonstrated ability to design experiments, evaluate model performance rigorously, and iterate on approaches based on empirical results
  • Experience building and maintaining ML pipelines-data ingestion, feature engineering, training, evaluation, deployment
  • Working knowledge of cloud-based ML infrastructure (Azure preferred; AWS or GCP acceptable)
  • Strong communication skills with the ability to explain model behavior, experimental results, and trade-offs to non-technical audiences
  • Self-directed with a track record of owning ML projects end-to-end-from problem formulation through production delivery-with minimal supervision

Preferred Qualifications
  • Master's or PhD in Computer Science, Statistics, Data Science, Engineering, Operations Research, or a related technical field
  • Experience in supply chain, operations, or logistics domains
  • Background in time series modeling, probabilistic methods, or optimization techniques applied to operational problems
  • Familiarity with Databricks, Spark, or similar distributed compute platforms for ML workloads
  • Experience with Azure services: Azure ML, Container Apps, App Configuration, DevOps pipelines
  • Experience working directly with enterprise customers to tune, validate, and explain model outputs in their specific business context
  • Experience with MLflow for experiment tracking and model versioning
  • Experience with Kafka or similar event streaming platforms for real-time data integration
  • Curiosity about the business processes your models serve and motivation to understand how supply chain decisions are actually made

Core Competencies
  • Customer Impact: Builds solutions with the end customer in mind-measures success by business outcomes, not model metrics alone
  • Analytical Depth: Goes beyond surface-level results to understand why models behave the way they do, especially when they fail-combines scientific rigor with practical problem-solving
  • Engineering Rigor: Writes production-quality code, designs reliable pipelines, and thinks about failure modes before they happen
  • Manages Complexity: Navigates messy real-world data and ambiguous problem definitions to deliver practical, scalable solutions
  • Communicates Effectively: Translates technical model behavior and experimental findings into clear narratives for product, services, and leadership audiences
  • Drives Results: Takes ownership, follows through on commitments, and delivers measurable improvements to customer outcomes

Technology Environment
Python, LightGBM, SQL, Azure (Container Apps, ML, DevOps), Databricks, Git/GitHub. Enterprise supply chain platform with SQL Server backends and REST APIs.
Why GAINS
- Work on software that leverages AI and ML to solve real logistics challenges for customers
- Direct impact on developer experience across the entire engineering org
- Collaborative, low-bureaucracy environment where engineers own their work end-to-end
- Competitive compensation and benefits
We are committed to equal employment opportunity and welcome everyone regardless of race, color, ancestry, religion, national origin, age, sex, gender identity, sexual orientation, disability, marital status, domestic partner status, veteran status or medical condition. We encourage people from all backgrounds to apply.

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