Top Entry Level Data Science Jobs

Analyze contact center, customer, operational, and Voice of Customer data to identify trends, performance gaps, and process improvements. Build dashboards, analytics workflows, AI solutions, automation tools, and prototypes using Python and related technologies. Partner with business and technical stakeholders, analyze call and CRM data, develop coaching recommendations, and present actionable insights and business cases that improve agent productivity, call quality, customer experience, and contact center operations.

The Data Scientist will support national security projects by developing and maintaining bulk data pipelines, processing structured and unstructured data, performing ETL and data quality analysis, and creating reports and dashboards. Responsibilities include using Spark, PySpark, Python, SQL, cloud services, and log-processing tools; documenting ETL designs; collaborating with customers and integration partners; and supporting Agile development. Preferred experience includes Databricks, Elastic MapReduce, Spark optimization, AWS services, and the ELK stack.

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.

Conduct research-oriented data analytics and algorithm development across diverse datasets and scientific domains. Apply machine learning, data mining, statistical methods, and neural network architectures to generate stakeholder insights. Curate and exploit small and large datasets, collaborate with subject-matter experts, document technical progress, prioritize tasks, and present results. Work may span embedded systems, cybersecurity, remote sensing, modeling and simulation, and scientific computation.

Analyzes transaction, settlement, and reconciliation data to identify discrepancies, trends, risks, fraud indicators, and process improvement opportunities. Develops dashboards, reports, scorecards, and visualizations; performs root-cause and statistical analysis; prepares and validates data; and translates findings into recommendations. Partners with clients, vendors, Accounting, Product, Engineering, and Operations teams to resolve issues, improve controls, support implementations, and communicate financial and operational insights.

Build and optimize machine learning classifiers, perform data mining, integrate third-party data, improve data collection, cleanse and validate datasets, conduct ad hoc analyses, present findings, and develop automated anomaly detection systems with ongoing performance monitoring.

Build predictive and machine learning models for patient needs, operational forecasting, churn, advocate quality, and risk. Design A/B tests and causal inference studies, apply advanced statistical methods, productionize models with data engineers, and communicate insights to business stakeholders. The role requires Python, advanced SQL, forecasting expertise, statistical proficiency, data privacy awareness, and the ability to work independently in a fast-paced healthcare startup.

Analyzes complex datasets using statistical and quantitative methods, creates decision-support visualizations, develops tools for monitoring data pipelines, performs ETL and data manipulation, and works with SQL, NoSQL, AI/ML, NLP, and computer vision technologies.

Partner with AI labs to improve frontier models by designing targeted data collection workflows, analyzing experimental results, and implementing quality controls. The role involves evaluating model behavior, reducing bias, supporting model safety and alignment, and translating technical findings into actionable insights for AI researchers and customers. Python is used for data analysis, pipeline development, and workflow prototyping.

Conduct research on how consumer LLMs search, reason, and synthesize information; design experiments and causal inference analyses; support product and engineering decisions with rigorous measurement; and build data pipelines and recurring evaluations that turn evolving research findings into product capabilities.

Develop data science systems for motion imagery and full-motion video programs. Analyze aircraft operations and perform multi-object tracking on live video. Work with operational, archived, and live data in Elastic, Oracle, and Kafka; develop Python-based solutions and deploy code to Kubernetes using current GPU technologies. Subject matter expertise is welcomed, while the role supports mission-focused government capabilities.

Analyze federal workforce data and position descriptions using statistical modeling, NLP, machine learning, and text analytics. Develop dashboards, visualizations, and reports; map duties and skills to job series and grade levels; validate findings with HR professionals and leadership; and provide recommendations supporting accurate, compliant job classification and reclassification decisions.

Own Alpaca’s growth data strategy across acquisition, activation, retention, referral, and monetization. Build experimentation frameworks, causal and incrementality analyses, funnel and cohort models, LTV/CAC and churn forecasts, marketing measurement, governed growth metrics, and self-serve analytics. Partner with Product, Marketing, Sales, Engineering, and Finance to influence strategy and roadmaps, communicate insights to leadership, mentor data professionals, and establish standards for growth analytics.

Supports equity research analysts by querying, transforming, analyzing, and combining large datasets; developing statistical models; building AI agents, MCP servers, and data workflows; and ensuring data quality, lineage, governance, and AI accessibility. The role translates complex technical methods into actionable insights, collaborates with research teams and vendors, and contributes to generative AI tooling and platform enhancements.

Supports equity research analysts by querying, transforming, analyzing, and combining large datasets; developing statistical models; and building AI tools, agents, MCP servers, and data processes. Ensures datasets meet accessibility, quality, lineage, and governance standards. Translates technical methods into actionable insights for nontechnical stakeholders while collaborating with research teams, vendors, management, and clients.

Administers UMM data-management infrastructure, including access controls, configuration, dashboards, data flows, ingestion validation, monitoring, and security compliance. Provides advanced tier-two technical support, troubleshoots issues, supports testing and user acceptance, documents configurations, and coordinates enhancements with engineering and data stakeholders. The role supports distributed DoD and Intelligence Community users and requires on-site work in a DIA SCIF with an active TS/SCI CI Poly clearance.

Develops data pipelines, curates and analyzes large datasets, builds quantitative models, validates data and results, and deploys analytics solutions with product teams. Responsibilities include discovering useful datasets, ensuring data quality and reliability, supporting customer-driven requirements, and integrating solutions into interconnected systems. The role requires strong Python and data-handling skills, database expertise, large-scale batch processing experience, and U.S. citizenship.

Optimize dataset quality and value through marketplace incentives, task targeting, bonuses, payouts, eligibility, and experimentation. Design treatment assignments and holdouts, evaluate incremental collection against objective functions, identify Goodhart effects, and translate findings into operational actions. Partner with software teams to ship incentive and payout changes into the collection product. Experience with marketplace experimentation, pricing, growth, real-money payouts, or product implementation is preferred.

Develop and administer Splunk environments, including distributed architecture, data pipelines, client and app management, integrations, searches, alerts, reports, dashboards, and configuration files. Automate tasks using Bash, Python, and PowerShell; develop custom apps and regular expressions; manage Linux systems; and interface programmatically with AWS using its SDK and CLI. An active TS/SCI clearance with full-scope polygraph is required.

The Cyber Data Science Engineer analyzes large cybersecurity datasets, develops data-driven solutions, evaluates vulnerability and compliance data, creates visualizations and reports, modernizes Excel-based situational awareness tools, and supports risk management automation and visualization.

Lead end-to-end deep learning development in a Databricks Lakehouse environment, including data pipelines, feature engineering, distributed training, model evaluation, inference, monitoring, and governed deployment. Build scalable batch and real-time ML systems using Spark, MLflow, and Databricks. Fine-tune large and foundation models, optimize performance and cost, and collaborate cross-functionally to productionize machine learning solutions.

Develops data science solutions by exploring, cleaning, analyzing, visualizing, and modeling structured and unstructured data. Works with clients to define analytical needs, develops algorithms and machine learning systems, leads complex solution development, and translates data into actionable insights. Potential applications include fraud detection, cancer research, and national intelligence. The role may involve NLP, text mining, AI engineering, LLMs, distributed computing, and production machine learning pipelines.

Analyze complex datasets to identify trends, risks, and opportunities; translate findings into business recommendations; present models and insights to senior leadership; collaborate with stakeholders; and develop analytical models, dashboards, reporting tools, and forecasting methodologies.

Analyze business problems using SQL, Python or R, and statistical methods to identify trends, test hypotheses, and develop insights or recommendations. Visualize findings in Tableau and collaborate with data engineers to present results to stakeholders. The role requires clear communication, flexibility, and the ability to explain analytical approaches and adapt deliverables to business needs.

Embeds with strategic customers to design, build, and implement AI-driven solutions that scale support operations and drive measurable Fin adoption. Analyzes customer workflows and data, establishes measurement best practices, prototypes production-aware tools, and partners with Sales, Success, Product, and Engineering. The role also feeds deployment insights into product strategy and requires strong communication, adaptability, statistical analysis, SQL, Python, and experience with AI/ML evaluation or LLM applications.