Jobs via Dice

Sr. Data Scientist

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Sr. Data Scientist on a 6-month remote contract, with a pay rate of "high possibility of extension." Key skills include advanced statistics, machine learning, Python or R, and experience with large datasets. A Master's degree or PhD is required, along with a current Principal Data Scientist Certification.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
-
🗓️ - Date
April 24, 2026
🕒 - Duration
More than 6 months
-
🏝️ - Location
Remote
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
United States
-
🧠 - Skills detailed
#Big Data #Mathematics #AI (Artificial Intelligence) #ChatGPT #Statistics #ADLS (Azure Data Lake Storage) #SAS #Azure ADLS (Azure Data Lake Storage) #Forecasting #SQL (Structured Query Language) #Azure #R #Time Series #ML (Machine Learning) #Tableau #Visualization #Matplotlib #Python #Security #Databricks #Cloud #Data Science #Spark (Apache Spark) #Data Processing #Data Ingestion #Storage #Datasets
Role description
Dice is the leading career destination for tech experts at every stage of their careers. Our client, Unisys, is seeking the following. Apply via Dice today! Sr. Data Scientist Remote 6 Months Contract with high possibility of extension Seeking a Senior Data Scientist who will work closely with client stakeholders to support economic analysis of national importance through application of advanced statistics and data science techniques and technologies. In this position, you will utilize your strong background in statistics, machine learning, generative AI, visualization, economic analysis, and big data processing to plan and execute projects to meet business client data needs. Requirements Partner with stakeholders to scope questions and assumptions, support solution design, and facilitate project execution Design and implement robust data ingestion, storage, integration, processing, retrieval, and management strategies for research datasets Build transparent, reproducible pipelines and analysis environments in cloud infrastructures Produce crisp exhibits and memos that explain methods, limitations, and uncertainty Facilitate and execute data-driven research by applying sophisticated statistical, machine learning, and computational methods to analyze complex datasets related to computer and information science Create compelling data visualizations and reports that convey complex research findings in a clear and accessible manner to both technical and non-technical stakeholders Support defensible analytics and econometric/causal inference workstreams, translating ambiguous business or legal questions into testable hypotheses and clear, client-ready findings Design and execute rigorous studies (e.g., difference-in-differences, panel models with fixed/random effects, instrumental variables/2SLS, time series/forecasting, etc.) to turn multi-source datasets into documented, auditable results Mentor teammates on best practices Stay updated on the latest academic research and industry advancements in data science, AI, and information systems, and apply relevant findings to ongoing projects Basic Qualifications: Master s degree, PhD preferred in Statistics, Mathematics, or a related quantitative field, with 5 8+ years of applied data science experience Mastery of Python or R, statistical tools such as Stata, SAS and strong SQL Expertise with ML algorithms (e.g. model selection, evaluation, feature engineering, etc.) Expertise with application of AI to deliver insights with optimal performance, cost savings, etc. using structured, unstructured data Expertise with data visualization tools (e.g. Tableau, Matplotlib) Experience processing large datasets, deploying LLMs in government cloud data platforms (e.g. Azure ADLS/Databricks/Azure ML, or equivalents) Comparative understanding of LLMs (e.g. Claude Code, ChatGPT), and tradeoffs in terms of capabilities, cost, and performance Excellent problem-solving skills and the ability to think critically and analytically to address complex research challenges Exceptional verbal and written communication skills and a bias toward rigor, clarity, and defensibility over black-box modeling are essential Familiarity with FISMA/NIST/Zero Trust security frameworks Current Principal Data Scientist (PDS) Certification Preferred Experience with Spark/Databricks. Domain exposure to antitrust, pricing, healthcare claims, fraud/forensics, or financial analysis is a plus. Experience producing reproducible, peer-reviewed-method analyses that meet Rule 702/Daubert reliability requirements and can withstand Daubert challenges (methods, error rates, standards/controls, and appropriate bounds on conclusions) Contribution to open-source projects or participation in relevant data science communities.