Top Entry Level Data Science Jobs in Atlanta, GA

Develops statistical, machine learning, predictive, and advanced analytical solutions using structured and unstructured public health data. Integrates and analyzes data from surveillance, research, and administrative sources; evaluates data quality; creates visualizations and reports; monitors analytical tools; and translates findings into actionable public health insights. Collaborates with multidisciplinary CDC program teams and communicates technical results to technical and non-technical audiences.

Prototype and validate analytical methods and LLM applications for political, media, and advertising data. Build proof-of-concept tools, datasets, pipelines, and applications, then engineer validated methods toward production. Apply statistical and data science techniques, maintain rigorous software development practices, communicate findings clearly, and collaborate with technical and non-technical stakeholders.

Analyze billions of user behavior, fund-flow, and market records using PySpark, Hive, Trino, and SQL. Build feature pipelines, investigate data anomalies, and develop and evaluate machine learning models for risk detection. Translate model performance into business metrics such as detection rate, false-positive rate, and latency while owning a project stream end to end.

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.

Conduct technical research at the intersection of AI and blockchain, exploring LLMs, agents, on-chain data, trading, and intelligent applications. Build reproducible prototypes and validate innovative ideas through proof-of-concepts and product demonstrations. Track industry developments, analyze research findings, document conclusions, and collaborate with algorithm and engineering teams to advance concepts toward implementation.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.

Develop user-facing financial AI and equity research algorithms across query understanding, document processing, entity recognition, event detection, retrieval, ranking, and quality evaluation. Build datasets, labeling systems, benchmarks, and production evaluation frameworks. Apply large language models, NLP, machine learning, reinforcement learning, knowledge graphs, and multimodal methods while collaborating with engineers to deploy and continuously optimize financial data and knowledge systems.

Early-career data scientist role to analyze user and content data, build metrics, design recommendation strategies (recall, ranking, cold-start, diversity), run A/B tests, evaluate experiments, explore LLM/multimodal applications, and improve data pipelines and feature logging.

Own end-to-end data science for emerging content surfaces, including product experimentation, causal inference, exploratory analysis, metrics, dashboards, and strategic insights. Identify opportunities to grow content supply, creators, and engagement while improving discovery. Lead content data architecture and quality initiatives, develop source-of-truth assets, and apply advanced statistical methods such as quasi-experiments and network testing. Partner with Product, Engineering, Design, and Research to influence product direction and communicate findings to diverse audiences.

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.

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.

Internship role to develop and deploy user-growth algorithms: user segmentation, recommendations, LTV/churn/conversion models, A/B testing, causal inference analyses, and collaborate with product and engineering to drive measurable growth.