Top Entry Level Data Science Jobs

Build data products and analytical models for Caterpillar’s product support teams. Responsibilities include large-scale data collection, processing, mining, statistical modeling, machine learning, NLP, text analytics, requirements analysis, and data visualization. The role translates complex insights into actionable recommendations and system requirements while collaborating with engineers, dealers, leaders, and internal customers. Work is onsite five days per week in Peoria, Illinois, with approximately 10% travel.

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.

Principal Data Scientist partnering with cross-functional teams to solve product and business problems through quantitative analysis, experimentation, forecasting, and product metrics. The role develops hypotheses, evaluates opportunities, influences product roadmaps, identifies challenges, and communicates data-driven insights and recommendations to leadership.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Develop predictive models and machine-learning solutions for cybersecurity, payment fraud detection, and vulnerability analytics. Analyze large datasets using distributed processing, identify patterns, create technical rules, integrate cybersecurity and transaction data, and design performance metrics. Collaborate with product, engineering, and operations teams, translate stakeholder needs into analytical solutions, and communicate insights to leadership. The role also involves graph databases and graph machine learning, including graph neural networks.

Develops data-driven analyses, algorithms, predictive models, and visualizations using structured and unstructured data. Partners with clients and stakeholders to uncover insights, improve processes, optimize workflows, and support financial, operational, and strategic decisions. The role involves data exploration, cleaning, mining, statistical programming, machine learning, and artificial intelligence applications. A Secret clearance or eligibility to obtain one is required.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Designs challenging data science and machine learning tasks, builds reproducible environments and verified reference solutions, and develops automated tests and evaluation frameworks for AI-generated code. The role analyzes AI agent performance, identifies bugs, leakage, overfitting, and methodological weaknesses, documents technical requirements, and iteratively improves task reliability and complexity.

The Data Scientist will develop reusable analytic tools, maintain reproducible workflows, manage and quality-assure complex datasets, conduct causal and non-causal analyses, apply machine learning and AI methods, review junior staff code, and translate technical findings into reports and presentations for clients and stakeholders.

Develops data-driven solutions for sustainment and defense clients by exploring, cleaning, analyzing, and visualizing structured and unstructured data. Builds predictive models, algorithms, and quantitative analyses using R or Python, machine learning, artificial intelligence, and natural language processing. The role applies data science to reliability, logistics, maintenance, and combat-system datasets and requires Secret clearance.

Manage and analyze disparate datasets to answer business questions, identify patterns, build predictive and machine learning models, and communicate results to stakeholders. The role includes data consolidation, reporting, model development, and automation of machine learning industrialization workflows. The Data Scientist will promote AI models to production in collaboration with data and platform engineers while supporting manufacturing, research, marketing, and other business teams.

Conduct basketball data science research using statistical modeling, machine learning, and complex datasets. Build and validate models evaluating players, teams, lineups, tactics, and decision-making. Develop reproducible Python or R workflows, tools, dashboards, and visualizations, partnering with Engineering to productionalize insights. Communicate findings and recommendations to front office, coaching, scouting, player development, and strategy stakeholders while advancing research validation and analytics workflows.

Analyzes structured and unstructured data to identify insights and solve client problems. Responsibilities include data exploration, cleaning, analysis, visualization, predictive modeling, quantitative analysis, algorithm development, and applying machine learning, artificial intelligence, or natural language processing. The role collaborates with clients and uses Python and other data technologies to produce objective, actionable answers for public- and private-sector challenges.

Conduct statistical and quantitative data analysis, build visualizations, develop software to monitor data pipelines, perform ETL and data manipulation, and work with SQL and NoSQL databases. Apply AI/ML techniques to complex datasets, including optional NLP and computer vision applications, to generate actionable insights and support government decision-making.

Analyze and design data architecture, develop and maintain operational data pipelines, optimize data processing workflows, and support database engineering across secure systems. Integrate APIs for querying, ETL, and migrations while using Python, JavaScript, or Java. Collaborate with technical and non-technical stakeholders to implement compliant data standards, labeling workflows, cloud architectures, and data transformation solutions.

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.

Provides research and data-analysis support to economists focused on monetary economics and financial markets in Asia and the Pacific. Responsibilities include preparing publications and presentations, collecting and validating data, reviewing methodologies, developing databases, identifying new data sources, applying advanced analytics, and collaborating on innovation projects. The role bridges economics, data, and technology in a multicultural international environment.

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.

Create challenging data science and machine learning tasks with reproducible environments, verified reference solutions, and robust evaluation tests. Analyze coding-agent failures, identify implementation errors, data leakage, and flawed assumptions, then refine tasks and document requirements, reasoning, and verification steps. The role requires practical programming, mathematical reasoning, and experience with competitive data science platforms such as Kaggle or DrivenData.

Develop and productionize AI/ML systems, train and integrate models, build ETL pipelines and databases, and support secure applications. Analyze data, research emerging AI technologies, create visualizations and analytic narratives, and collaborate with customers to define solutions. The role requires expertise in machine learning frameworks, programming, statistics, algorithms, software architecture, and AI model deployment, plus an active TS/SCI clearance with polygraph and U.S. citizenship.

Supports AbbVie’s research and discovery teams by building data integrations, visualizations, dashboards, and analytics solutions. The role transforms and captures complex scientific data, including omics, biomarkers, and preclinical safety data; gathers requirements; documents data flows; supports scientists; and collaborates with technology stakeholders. Requires Python and SQL proficiency, familiarity with visualization and data pipelining tools, software development processes, CI/CD, and modern data science and AI techniques.

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.