Top Entry Level Data Science Jobs

Designs and maintains reliable ETL/ELT pipelines, data lakehouse architectures, schemas, scheduling, monitoring, and data quality frameworks in an air-gapped environment. Partners with engineering and testing stakeholders to deliver technical data solutions, integrates APIs with Jira and Confluence, supports Ubuntu infrastructure, and uses Python, PySpark, PyIceberg, GitLab, Docker, PostgreSQL, and CI/CD practices.

Develop and maintain ETL workflows and data pipelines using Python, Apache NiFi, shell scripts, and APIs. Ingest, transform, validate, and deliver data for mission applications, analytics, and data science. Support Kubernetes workloads, CI/CD, automated testing, Linux administration, monitoring, troubleshooting, and data quality improvements. Collaborate with software, platform, mission, and data science teams in secure environments while documenting workflows and participating in agile delivery.

Research and develop hypotheses using diverse real-world datasets, apply advanced methodologies to extract meaningful signals, and rigorously test theories with engineers and business stakeholders. The role involves literature reviews, quantitative data analysis, clear communication of complex ideas, and ongoing engagement with the academic research community.

Build and own Legora’s Growth data foundation, including trusted dbt models, data pipelines, shared GTM metrics, dashboards, and data activation into business systems. Partner with Marketing, Sales, RevOps, and Customer Success to analyze funnels, attribution, pipeline, retention, expansion, and customer behavior. Improve data quality, conduct business deep dives, and establish scalable data-team standards and tooling.

Develop and validate a six-week Snowflake data-sharing pilot for operational risk monitoring. Build curated datasets and views, implement data contracts and access controls, reconcile data, test quality, create monitoring queries, document support procedures, and mentor colleagues in statistical computation and visualization. The role requires experience with big data technologies, statistical tools, machine learning, complex models, visualization platforms, and enterprise data documentation.

Develop forecasts, machine learning models, and optimization algorithms for renewable energy and battery storage assets. Analyze large energy-market datasets, build simulations, deploy production-grade data science workflows, create dashboards, and establish asset-optimization metrics. The role collaborates with engineering, operations, and energy markets teams to support real-time operations, market strategies, and business development.

Designs AI solutions for fund administration workflows, selecting models, prompts, retrieval, agents, rules, or human review. Builds evaluation datasets and metrics, runs offline and pilot evaluations, analyzes failure patterns, measures accuracy, efficiency, and cost savings, and packages successful approaches into reusable patterns.

Evaluate and improve AI mitigation systems, classifiers, and detection pipelines across high-risk domains. Build monitoring and measurement frameworks, analyze false positives and negatives, identify adversarial trends, quantify customer impact, and recommend prioritized interventions. The role requires rigorous experimentation, causal analysis, dashboarding, and cross-functional collaboration with engineering, product, research, and executive stakeholders to improve AI safety and preparedness.

Develops data science, business intelligence, predictive risk models, dashboards, ETL processes, and AI/ML solutions for a federal client. Uses Power Platform, SharePoint, SQL, R, Python, and related tools to automate workflows, analyze large datasets, integrate enterprise data, assess cybersecurity and business risks, and deliver actionable insights and visualizations to organizational leaders.

Perform exploratory data analysis, statistical research, machine learning, predictive modeling, and visualization to generate actionable business insights. Own governance, adoption, and integration of a global supply chain digital tool, serving as the SIOP point of contact. Translate operational needs into technical requirements, improve data quality, coordinate stakeholders, drive change management, and partner with digital technology teams across a matrixed organization.

Designs and optimizes scalable data pipelines and data solutions supporting actuarial modeling and assumption setting. Ensures data quality, governance, validation, monitoring, and audit controls across complex financial-services datasets. Translates business requirements into technical solutions, integrates data sources, applies machine learning and advanced analytics, and improves platform performance. Provides technical guidance, reviews code and analysis, coaches junior colleagues, and contributes to data engineering standards and continuous process improvement.

Conduct exploratory data analysis, statistical research, and machine learning to identify patterns, build predictive models, and deliver actionable recommendations. Translate customer collection priorities into technical strategies, plans, and requirements. Analyze historical data, assess mission effectiveness, maintain traceability documentation, and liaise between customers and engineering teams. The role requires UNIX/Linux troubleshooting, strong communication, customer engagement, and an active TS/SCI clearance with CI polygraph.

Lead modeling, simulation, and analysis of satellite systems, sensing performance, space targets, and Space Domain Awareness missions. Engage stakeholders, lead small teams, present data-driven findings, support strategic decision-making, and develop space protection tactics, techniques, and procedures.

Build trusted financial and business metrics, analytical datasets, dashboards, and recurring reports. Investigate performance drivers and variances, translate ambiguous questions into analyses and recommendations, support forecasting and planning, improve data quality with Data Engineering, and communicate findings to Finance and business stakeholders.

Design, develop, and maintain production-ready machine learning models and AI solutions supporting student engagement, recruitment, and service delivery. Build scalable applications, data pipelines, ETL/ELT processes, and enterprise integrations using Python, Spark, and modern cloud platforms. Translate analytical outputs into practical recommendations, collaborate with technical and business stakeholders, and contribute to the University's evolving data science and AI capability.

Leads product vision, strategy, and roadmap for data science-enabled products and AI solutions. Partners with data scientists, engineers, designers, and business teams to define requirements, develop machine learning models, deploy production solutions, and monitor performance. Builds scalable Databricks and Hadoop data pipelines, Tableau dashboards, forecasting capabilities, anomaly detection, and SPC alerting. Uses SQL, Python, and R to analyze operational data, predict wafer defectivity and removal rates, improve supply-chain forecasting, and guide experimentation and investment decisions.

Analyze data to identify trends and anomalies, develop machine learning and statistical models, perform data parsing and ETL with Python and SQL, create dashboards, and collaborate with data engineers. The role emphasizes NLP, LLM, and Generative AI applications, quantitative analysis, model validation, and experimentation with emerging tools for federal clients.

Provide data science and analytical support for evaluating, modernizing, and optimizing complex enterprise systems. Collect, process, analyze, and visualize operational and technical data; develop metrics, dashboards, models, reports, and automated processes; investigate anomalies and performance issues; assess data quality; and communicate recommendations to government stakeholders. Collaborate with engineering, software, architecture, cybersecurity, and mission teams to support data-driven government decision-making and operational planning.

Create reproducible data science and machine learning tasks with reference solutions, robust tests, and evaluation criteria. Investigate coding-agent failures, identify errors, data leakage, and flawed assumptions, then refine tasks accordingly. Document requirements, solution reasoning, and verification steps. The role requires competitive data science experience, mathematical reasoning, programming ability, and practical knowledge of machine learning and quantitative modeling.

Develop advanced statistical and machine learning models for national security missions. Analyze structured and unstructured data, engineer processing workflows, integrate authoritative sources, create dashboards, and deliver decision-support products. The role supports cloud-based analytical environments, data architecture, governance, operational assessments, and mission planning. Responsibilities include translating stakeholder needs into analytical solutions, documenting methodologies, presenting findings, and improving data quality across mission systems.

Analyze complex banking datasets using econometrics, statistical modeling, machine learning, and predictive analytics. Develop models supporting loan and deposit performance analysis, risk management, customer experience, and operational efficiency. Build data pipelines, ensure data quality, create visualizations, communicate recommendations to stakeholders, and collaborate across IT, marketing, and risk teams. Provide mentorship to junior data scientists and analysts.

Build, retrain, validate, and maintain data-driven models for sports, concerts, and live events. Investigate data quality issues and unexpected model behavior, fulfill recurring and ad hoc modeling requests, and use Python and SQL to manipulate datasets and troubleshoot workflows. The role also supports internal application development, debugging, automation, process improvements, and cross-functional collaboration to deliver accurate and reliable modeling outputs.

Develop advanced Bayesian models and stochastic simulations for business modeling, product innovation, user behavior, and system performance. Manage analytical projects end to end, including data preparation, model development, validation, deployment, and proof-of-concept applications. Communicate methodologies, progress, and findings through reports and stakeholder presentations.

Applies statistical analysis, data mining, machine learning, NLP, and predictive modeling to human performance, behavioral health, readiness, and program data. Prepares and validates datasets, develops dashboards and reports in Power BI, automates recurring analyses, documents reproducible workflows, and communicates findings to government, clinical, performance, and command stakeholders. Maintains data quality, security, privacy, HIPAA, and DoD IL5 controls while supporting decision-making and program-efficacy evaluations.

Analyze large datasets to generate business and research insights for national security customers. Develop NLP, text classification, topic modeling, semantic search, and generative language model solutions using Python and deep learning frameworks. Perform statistical analysis, model evaluation, data collection, visualization, and SQL-based data extraction. Communicate methodology and findings to senior leadership, build API and database integrations, support cloud applications, and tune LLMs for mission-specific use cases.