Senior Data Scientist, AI/ML

C. Mack Solutions

. Frame mission and business questions as analytical or machine learning problems and define success measures with stakeholders and product owners .

Posted 10/27/2026full-timeUnited StatesSenior💰 $125,000 - $175,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data science and machine learning, with strong proficiency in Python and its data science stack, experience in building and evaluating machine learning models, and familiarity with MLOps practices. Capable of effectively communicating complex technical results to non-technical stakeholders and collaborating in agile environments.

Highest-signal resume keywords

Python ProficiencyMachine Learning Model DevelopmentMLOps PracticesData Visualization ToolsGenerative AI Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data PreparationStatistical AnalysisDeep Learning FrameworksSQL SkillsNoSQL Data StoresAPIs IntegrationLarge Language ModelsFeature EngineeringModel EvaluationData Processing Tools

Soft Skills

Excellent Communication SkillsProblem-SolvingMentoringCollaborationAdaptability

Tools & Technologies

PandasNumPyScikit-learnJupyterPyTorchTensorFlowTableauPower BIAWS SageMakerGoogle Cloud Vertex AI

Certifications & Qualifications

Google Cloud Professional Machine Learning EngineerAWS Certified Machine Learning Engineer – AssociateAWS Certified AI Practitioner

Industry Keywords

Data ScienceMachine LearningGenerative AIMLOpsAgile MethodologyResponsible AIFederal AI Risk ManagementComputer VisionGeospatial DataRemote Team Collaboration

Tech Stack

Tools & technologies

AWSBigQueryCloudDockerDynamoDBMongoDBNoSQLNumpyPandasPythonPyTorchScikit-LearnSparkSQLTableauTensorflow

About the role

Key responsibilities & impact

Frame mission and business questions as analytical or machine learning problems and define success measures with stakeholders and product owners
Explore, clean, and prepare structured and unstructured data from SQL and NoSQL sources; engineer features
Develop, train, validate, and tune statistical and machine learning models for AI/ML pilots and predictive analytics
Evaluate model performance using appropriate metrics, validation strategies, and error analysis; monitor deployed models for drift and bias
Apply large language models, including Google Gemini, to summarization, extraction, classification, and retrieval-augmented generation use cases
Design prompts, grounding data, and evaluation sets; build repeatable GenAI output evaluation approaches
Partner with AI/ML engineers and developers to integrate models into applications through APIs
Help move prototypes toward production using MLOps practices including versioning, reproducibility, deployment, and monitoring
Communicate findings through visualizations, dashboards, and briefings, including uncertainty and limitations
Document data sources, methods, assumptions, and model behavior for reproducibility, governance, and responsible-AI review
Collaborate on prototyping teams to deliver working software applications on short timelines
Analyze complex project-related problems and create innovative technology, methodology, tool, and solution components
Participate in Agile delivery phases and ceremonies, including release and sprint planning, artifact creation, sprint testing, demonstrations, and retrospectives
Work independently on complex tasks with little direction and mentor less experienced team members

Requirements

What you’ll need

Must live within the DC, MD, VA area
U.S. Citizenship is required
Must be able to obtain a CBP Public Trust clearance
Bachelor’s degree (BA/BS) in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field
7+ years of experience in data science, machine learning, or a related quantitative/analytical role
Strong proficiency in Python and its data science stack (e.g., pandas, NumPy, scikit-learn, Jupyter)
Experience with at least one deep learning framework (PyTorch or TensorFlow)
Strong SQL skills
Experience with NoSQL data stores (e.g., MongoDB, DynamoDB)
Solid foundation in statistics, including probability, regression, hypothesis testing, sampling, and experimental design
Hands-on experience building and evaluating machine learning models end to end, from data preparation through validated results
Practical generative AI experience: working with LLMs, prompt engineering, embeddings/RAG, and evaluating LLM outputs
Experience with data visualization (e.g., matplotlib, seaborn, Plotly) and/or BI tools (e.g., Tableau, Power BI, Looker)
Experience working with APIs and common data formats (JSON, YAML)
Ability to analyze technically complex problems and develop and implement new and innovative solutions in a fast-moving, fluid software prototyping environment
Excellent written and verbal communication skills, including the ability to explain technical results to non-technical stakeholders
Google Gemini and Google Cloud Vertex AI experience strongly preferred
Experience with AWS SageMaker or Bedrock valued
Master’s or Ph.D. in a quantitative field preferred
Experience working with live data and video streams; computer vision experience a plus
MLOps experience (e.g., MLflow, model registries, containerized deployment with Docker) and CI/CD solutions such as GitLab
Experience with large-scale data processing tools such as Spark, Databricks, or BigQuery
Experience with geospatial or sensor data
Familiarity with responsible-AI practices and federal AI risk management (e.g., NIST AI Risk Management Framework)
Technical certifications such as Google Cloud Professional Machine Learning Engineer, AWS Certified Machine Learning Engineer – Associate, or AWS Certified AI Practitioner
Federal experience, ideally within DHS or a law enforcement / regulated environment
Experience working in a geographically dispersed, remote team

Benefits

Comp & perks

Medical, dental, and vision insurance
Retirement savings plans
Paid time off
Other supplemental programs