Data Scientist - W2 Requirement

Role: Data Scientist

Position ID #: #290

Location: Rockville, MD (3 days onsite & 2 days remote)

Duration: 6 month base contract; long-term extensions

Work Authorization: Any

Interview: 1 virtual; 1 onsite; Offer

Notes:

Best fit

Senior Data Scientist with strong Python, statistics, production ML, SQL, PySpark, Plotly Dash, and cloud deployment experience.

Skill area

Priority

What to look for

Python

Critical

Advanced hands-on development for analysis, modeling, and automation

Statistics

Critical

Hypothesis testing, experimental design, statistical validation

Machine learning

Critical

Building, evaluating, and deploying models in production

pandas and SQL

Critical

Data manipulation, querying, transformation, analytical datasets

Plotly / Plotly Dash

Critical

Building interactive dashboards and visual applications

PySpark

High

Large-scale distributed data processing

AWS, Azure, or Google Cloud Platform

High

Cloud data processing and model deployment

Communication and stakeholder management

High

Business requirements, executive communication, influencing decisions

Generative AI / LLMs

Preferred

LLM applications, prompt engineering, generative AI workflows

Graph analytics

Preferred

Graph databases, network analysis, graph algorithms

MLOps

Preferred

Model monitoring, lifecycle management, deployment best practices

Financial services

Preferred

Familiarity with regulated environments and business constraints

Job Description:

About the Role

We are seeking an experienced Senior Data Scientist to join our team and drive impactful data-driven insights that inform strategic business decisions. This role requires a blend of technical expertise, analytical rigor, and excellent communication skills to collaborate effectively across technical and non-technical stakeholders.

Key Responsibilities

· Design, develop, and deploy machine learning models to solve complex business problems

· Explore and implement generative AI solutions to enhance analytical capabilities and business processes

· Create advanced, interactive visualizations and dashboards to communicate insights to diverse audiences

· Collaborate with cross-functional teams to understand business needs and translate them into analytical solutions

· Conduct rigorous statistical analyses to validate findings and ensure data integrity

· Lead stakeholder engagement, presenting complex technical concepts in accessible ways

· Mentor junior team members and contribute to the growth of the data science practice

· Deploy and maintain models in cloud-based environments

· Drive end-to-end project delivery from problem definition through implementation

Required Qualifications

· Python Proficiency: Expert-level skills in Python for data analysis, modeling, and automation (required)

· Data Manipulation & Processing: Strong proficiency in pandas for data analysis and PySpark for large-scale distributed data processing

· SQL: Advanced SQL skills for data extraction, transformation, and analysis across various database systems

· Statistics: Strong foundation in statistical methods, experimental design, and hypothesis testing (required)

· Machine Learning: Hands-on experience building, training, and deploying ML models in production environments

· Advanced Visualization: Proficiency with Plotly and Plotly Dash for creating interactive, production-grade visualizations

· Cloud Technologies: Experience with cloud platforms (AWS, Azure, or Google Cloud Platform) for data processing and model deployment

· Communication Skills: Exceptional ability to communicate technical findings to both technical and non-technical audiences

· Stakeholder Management: Proven track record of managing stakeholder relationships and driving alignment

· Education: Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, or related quantitative field; advanced degree (Master's or PhD) preferred

Preferred Qualifications

· Generative AI: Experience with large language models (LLMs), prompt engineering, and GenAI applications

· Experience with graph databases and graph analytics

· Knowledge of network analysis and graph-based algorithms

· Experience in financial services or regulated industries

· Familiarity with MLOps and model monitoring best practices