Top Entry Level Data Science Jobs

Develop and evaluate foundational deep learning methods, including transformers, embeddings, and neural networks, for credit modeling and loss prediction. Integrate models into ML pipelines, apply representation learning to financial data, and build specialized AI agents for data science and business workflows. Collaborate with technical and product teams, communicate findings, mentor colleagues, and measure model impact on customer and business outcomes.

Develop partner-specific pricing strategies for products and bundles, analyze volume, conversion, cost, and competitive data, and identify revenue opportunities. Build AI-powered pricing calculators, deal-sizing models, dashboards, and experimentation frameworks. Collaborate with Finance, Partnerships, Product, Compliance, and Legal to ensure commercially sound pricing. Establish scalable pricing frameworks, reduce escalations, document decisions, and communicate complex pricing recommendations to senior stakeholders and non-technical partners.

Build production resource-allocation capabilities in Palantir Foundry using simulation, forecasting, optimization, and statistical models. Develop tested Python, quantify uncertainty, validate assumptions with operational experts, benchmark models, identify infeasible requests, and assess organizational impact. The role requires onsite work in the Washington, DC area and collaboration with domain specialists.

Entry-level Data Scientist responsible for framing analytical problems, preparing and validating data, building statistical and machine-learning models, evaluating performance and uncertainty, and communicating findings. The role involves creating visualizations and reproducible analytical workflows, collaborating with engineering and product teams, and helping transition prototypes into monitored solutions. Candidates should have quantitative academic training, hands-on Python or R experience, and familiarity with data analysis tools. The position is based in Greenville, South Carolina and requires unrestricted U.S. work authorization.

Develop advanced analytical models, predictive forecasts, and AI/ML capabilities supporting Navy Reserve readiness and mission assurance. Build analytics products, dashboards, and visualizations; apply statistical analysis, machine learning, data mining, and predictive modeling to identify risks and optimize resources. Collaborate with engineers, analysts, and government stakeholders, evaluate emerging technologies, communicate recommendations, and potentially lead analytic workstreams.

Create reproducible data science and machine learning tasks with verified reference solutions, robust tests, and evaluation criteria. Investigate coding-agent failures, identify implementation errors and data leakage, refine task designs, and document requirements and verification steps. The role requires competitive data science experience, mathematical reasoning, programming ability, and practical expertise in implementing and validating solutions.

Build and validate forecasting, optimization, anomaly detection, and measurement models using complex customer data. Perform exploratory analysis, experiments, backtesting, and error analysis; partner with ML and data engineers to productionize models, features, evaluation pipelines, and monitoring. Translate business questions into analytical recommendations and communicate findings to technical and non-technical stakeholders. The role is hybrid in New York City or remote in the U.S. with monthly travel to New York.

Develop personalization and machine learning solutions across product, growth, and monetization. Own projects from opportunity discovery and data preparation through modeling, experimentation, product integration, launch evaluation, and iteration. Build production scoring workflows, translate predictions into product actions, apply causal reasoning, and collaborate with engineering and product teams. Projects include recommendations, promotion targeting, retention, customer lifetime value, and lifecycle management.

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.

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.

The Data Scientist I will clean and structure messy datasets, engineer features, run exploratory and numerical analyses, fit and evaluate statistical and machine learning models, and support experiments. The role involves writing documented Python code, monitoring model performance, and collaborating with researchers and engineers. This early-career position offers mentorship while progressively increasing ownership of modeling projects involving online narratives, public opinion, AI, machine learning, and causal inference.

Develop advanced analytical models, predictive forecasting solutions, AI/ML capabilities, dashboards, and visualization tools supporting Navy Reserve readiness and operational optimization. Apply statistical analysis, machine learning, data mining, and predictive modeling to identify risks and improve decisions. Collaborate with technical teams and government stakeholders, evaluate emerging analytics technologies, communicate findings, and potentially lead analytic workstreams and mentor junior staff.

Develop reproducible emissions calculations and analytical methodologies using Python, SQL, and PySpark. Investigate complex energy and emissions datasets, identify data-quality issues, apply statistics and machine learning, and translate research into scalable production capabilities. Collaborate with engineering, product, research, and customer teams while contributing maintainable code through Git, testing, documentation, and code review.

Analyze product and operational data to guide roadmap decisions, define metrics, evaluate experiments, uncover user friction, and build reliable dbt models, dashboards, and analytical tools. The role partners closely with Product and Operations, improves data quality and instrumentation, and translates complex findings into actionable recommendations. It requires strong SQL, Python, experimentation, data modeling, visualization, and product judgment skills.

Supports the design, validation, and operationalization of predictive, machine learning, and AI models. Analyzes structured and unstructured data, develops algorithms, validates outcomes, creates visualizations, and communicates findings to stakeholders. Collaborates cross-functionally to integrate solutions and prepares model pipelines for business use. The role also involves statistical analysis, NLP, exploratory data analysis, feature engineering, and research into emerging technologies.

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 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.

Own Junction’s data science research, statistical modeling, experimentation, and evaluation strategy across diagnostics, clinical workflows, and AI-enabled products. Build predictive and decision-support models, establish measurement and quality frameworks, partner with engineering to deploy reliable systems, and communicate findings to technical and executive stakeholders. The role combines strategic roadmap ownership with hands-on analytical work in a high-ambiguity startup environment.

Develops and deploys predictive models and machine learning solutions for clients. Leads or mentors analysts, developers, and data consumers; designs cloud data environments, pipelines, statistical models, and data visualizations. Transforms structured and unstructured data into actionable insights, validates and engineers features, and advises clients on applying predictions to mission objectives. The role requires Secret clearance and a bachelor's degree, with DoD data platform and advanced clearance experience preferred.

Develops and maintains data pipelines, analyzes structured and unstructured data, and applies statistical, machine learning, NLP, forecasting, clustering, and predictive modeling techniques. Uses Python, R, distributed processing frameworks, cloud data platforms, and visualization tools to solve complex national security data challenges. Collaborates with data engineers, automates data processes, develops algorithms, and communicates analytical findings to nontechnical audiences.

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.

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 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.