Top Entry Level Data Science Jobs

Develop predictive models and data science solutions for automated credit-risk and lending decisions. Responsibilities include collecting, cleaning, visualizing, and analyzing large datasets; identifying patterns; presenting insights to stakeholders; translating models into business strategies; partnering with IT on implementation; and monitoring model performance.

Develop and deploy machine learning models for fraud detection and risk assessment. Analyze large transactional datasets using exploratory analysis, Python, and data science libraries to uncover anomalies and patterns. Collaborate with engineering and business teams to integrate models into production, monitor performance, and improve algorithms. The role also involves researching emerging fraud detection and AI techniques.

Owns enterprise-level business and intelligence analysis by integrating structured and unstructured data, developing repeatable methodologies, creating Tableau visualizations, and translating complex findings into clear narratives. The role interprets data models, transforms data with Python or similar languages, applies quantitative and qualitative methods, documents workflows, coordinates customer requirements, and presents insights to technical and non-technical stakeholders. An active TS/SCI clearance with polygraph is required.

Develops and manages AI and machine learning product roadmaps, leads cross-functional teams, oversees model development and deployment, and monitors model performance. Responsibilities include data preparation, exploratory analysis, LLM and NLP integration, governance, bias mitigation, compliance, risk management, stakeholder communication, product optimization, Agile delivery, and mentoring junior staff.

Develop and apply advanced AI and machine learning methods to aging biology research. Build computational approaches for large-scale biological and imaging data, including computer vision, segmentation, deep learning, generative AI, diffusion, multimodal, and foundation models. Analyze complex datasets, identify biologically meaningful patterns, collaborate with experimental and computational researchers, and help shape research at the intersection of AI, imaging, and aging.

Develop AI-enabled systems for harmonizing, modeling, and interpreting large biomedical and multi-omics datasets. Build LLM-powered tools, agentic workflows, RAG systems, data pipelines, and predictive models supporting computational biology and aging research. Collaborate with scientists and engineers on data curation, literature mining, annotation, reproducibility, documentation, manuscripts, and research software. Apply machine learning, network analysis, clustering, classification, and biological interpretation to transcriptomics, proteomics, metabolomics, clinical, imaging, and phenotypic data.

The job description does not include specific responsibilities or qualifications. The role is identified as Principal Data Scientist and may be performed remotely, in an office, or in a hybrid arrangement, depending on the employee’s location and Atlassian’s legal entity availability.

The Data Scientist will support national security projects by developing Python-based geospatial solutions and managing AWS resources, including EC2, Lambda, S3, and IAM policies. Preferred work includes deploying applications with AWS CDK and containers, maintaining search engines and SQL/NoSQL databases, using other cloud platforms, and holding relevant AWS certifications. An active TS/SCI clearance with polygraph is required.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.

Join a two-year Data Science Accelerator Program to rotate through analytical product development assignments. Assist with data cleaning, merging, feature creation, model validation, visualization, and client inquiries while learning statistical and machine learning techniques under senior data scientists' guidance.

Analyze organizational data to solve business problems, reduce costs and errors, identify statistically significant trends, and provide actionable recommendations. Pull data from SQL databases, analyze it using Python or R, visualize findings in Tableau, and communicate insights clearly to stakeholders. Collaborate with data engineers and adapt analytical approaches based on project needs.

Designs and develops methods to consolidate, cleanse, integrate, and analyze structured and unstructured big data. Performs statistical and data-mining analyses using R, SAS, and MATLAB; designs data acquisition systems and databases; gathers requirements; and produces reports. Develops analytical models, algorithms, and automated processes using SQL, PHP, and Python to support manufacturing and process information products.

Develops statistical and data-mining models, automated analytical processes, and systems for consolidating large structured and unstructured datasets. Uses R, SAS, MATLAB, SQL, PHP, and Python to cleanse, integrate, and evaluate data. Partners with internal customers, product teams, service teams, and business leaders to define requirements, identify KPIs, derive insights, support manufacturing and process information products, and communicate analytical findings that improve business processes.

Translates stakeholder requirements into automated, data-driven solutions. Designs end-to-end workflows for data ingestion, storage, transformation, quality, governance, and delivery across structured and unstructured data. Modernizes data environments, improves accessibility and lineage, and reduces manual processes using Microsoft 365 and related platforms. Collaborates across teams in an agile environment, prioritizes development work, tracks progress, and advises stakeholders on data strategy and AI readiness.

Analyze credit strategies and lending performance using SQL queries, complex data aggregations, and quantitative methods. Evaluate credit policy changes, monitor portfolios, identify business improvement opportunities, and present data-backed recommendations to Risk, Operations, and other stakeholders. Collaborate with data vendors and senior leaders to support AI-driven lending and strategic decision-making.

Develop and deploy machine learning models, design experiments and A/B tests, productionalize data science code with GitHub and MLOps pipelines, and collaborate with data vendors. The role offers hands-on experience with cloud infrastructure, advanced machine learning tools, and production AI systems while working alongside senior engineering and data science leaders.

Work as a healthcare data scientist/consultant delivering statistical analysis, predictive modeling, BI reporting, and data visualization for clients. Independently manage projects and client relationships, using R, SAS, SPSS, Cognos, and BusinessObjects. Report to the Senior Consultant.

Support government analytics for integrated combat and weapons systems using MATLAB or Python. Analyze test data, develop automation scripts, produce technical reports and visualizations, collaborate with multidisciplinary teams, and improve data analysis workflows under government direction.

Build coupled fusion-device simulation models, radiation and surrogate models, diagnostic data pipelines, Bayesian inference systems, and uncertainty quantification tools. The role covers pulsed-power circuits, plasma dynamics, neutron and X-ray production, machine lifetime modeling, and HPC-enabled computation. The scientist will write production-grade numerical software, validate models against experimental diagnostics, document assumptions, and eventually grow a simulation team.

Lead advanced marketing analytics engagements, including media mix modeling, attribution, forecasting, experimentation, and optimization. Develop marketing science frameworks, bespoke analytical offerings, proprietary marketing technology, and new modeling datasets. Communicate statistical findings to clients and senior stakeholders, influence implementation, and support new business development and client relationships.

Leads data science research and policy solutions to improve justice-process efficiency, reduce incarceration, and support public safety. Responsibilities include exploratory analysis, predictive and machine learning modeling, production deployment and monitoring, reproducible workflows, dashboards, policy briefs, presentations, stakeholder engagement, and cross-agency project management.

Leads the design, development, and deployment of production-scale AI and machine learning systems. Builds solutions using LLMs, generative AI, NLP, computer vision, and deep learning; architects ML pipelines; optimizes models through fine-tuning and prompt engineering; translates technical results into business strategies; partners with stakeholders; communicates outcomes; and mentors data scientists while establishing MLOps best practices.

Support human performance initiatives by entering, cleaning, and preparing data, maintaining databases/spreadsheets, and assisting basic performance analyses. Collaborate with multidisciplinary practitioners to identify data collection opportunities and prepare reports/presentations conveying trends and readiness insights. Use government systems to manage and analyze HP data.

As a Data Scientist, you will collaborate with scientists to develop algorithms and software solutions for data analysis and machine learning, while engaging in multidisciplinary teamwork to meet project objectives.

Design, develop, validate, operationalize, and maintain large-scale data models and analytics. Perform end-to-end analytics including data wrangling, feature engineering, modeling (including NLP and ML), MLOps, and stakeholder communication to drive business insights and improve operational efficiency.