Data Scientist - AI/ML

TechDigital Group; Atlanta (GA); On-site; USD 85,000 - 115,000; Full time

Job summary

A leading technology firm in Atlanta is seeking a skilled data scientist with strong experience in Python and machine learning. The role involves developing analytical frameworks and communication of key insights to stakeholders. Candidates should be proficient in modern ML libraries and techniques, with familiarity in LLM application frameworks. This opportunity emphasizes collaboration with engineering and business teams, making it vital to possess strong analytical and communication skills.

Qualifications

• Strong Python-based data science and machine learning experience.
• Hands-on exposure to modern AI and LLM frameworks.
• Experience with cloud-based ML platforms and distributed data processing.

Responsibilities

• Develop analytical frameworks and measurement strategies for products.
• Design and analyse complex business and user experiments.
• Translate complex technical concepts into business insights.

Skills

Python proficiency

Data exploration

Machine learning experience

Statistical modelling

SQL proficiency

Tools

NumPy

Pandas

Scikit-learn

PyTorch

TensorFlow

Job description

Note: Client are looking for candidates with strong Python-based data science and machine learning experience, combined with hands‑on exposure to modern AI/LLM frameworks and agentic AI development on Cloudera / Databricks.

Job Description

• Join Marketing Services Technology team to develop analytical frameworks and reliable measurement strategies for various products, services, and capabilities.
• Design, execute, and analyse complex business and user experiments.
• Partner with Product partners and other Data Engineers to set the vision and develop experimentation specifically focused on Profiling Engine, Advanced Segmentation Engine and Advanced Targeting.
• Communicate key insights from analyses, experiments, and data products to stakeholders.

Core Data Science & Analytics

• Demonstrate strong expertise in data exploration, feature engineering, statistical modelling, and predictive analytics, with the ability to operationalise models in production environments.
• Have deep proficiency in Python (preferred) and/or R, with experience using modern data science libraries such as NumPy, Pandas, Scikit‑learn, and PyTorch or TensorFlow.
• Be highly proficient in SQL and experienced in working with large‑scale data warehouses and pipelines.

Machine Learning & AI Engineering

• Possess strong experience developing, evaluating, and deploying machine learning and deep learning models across the model lifecycle.
• Experience building and deploying models using modern ML and MLOps practices, including experiment tracking, model versioning, CI/CD for ML, and monitoring.
• Familiarity with cloud‑based ML platforms (AWS preferred) and distributed data processing frameworks (e.g., Spark).

Generative AI & Agentic Systems

• Hands‑on experience with Large Language Models (LLMs) and generative‑AI frameworks, including prompt engineering, retrieval‑augmented generation (RAG), and model orchestration.
• Experience building AI agents or agentic workflows capable of reasoning, tool use, multi‑step task execution, and autonomous decision‑making.
• Familiarity with LLM application frameworks (e.g., LangChain, LlamaIndex, or similar orchestration frameworks).
• Experience with vector databases, embeddings, and semantic search for building knowledge‑driven AI systems.

Agentic Coding & AI‑Assisted Development

• Strong understanding of AI‑assisted software development workflows, including agent‑based coding, code generation, automated debugging, and evaluation loops.
• Experience integrating LLMs with APIs, internal tools, and data systems to build production‑grade AI copilots or autonomous workflows.

Business Impact & Communication

• Ability to translate complex technical concepts into clear business insights, communicating effectively with both technical and non‑technical stakeholders.
• Strong analytical thinking with the ability to work with ambiguous or incomplete data, develop creative analytical approaches, and connect results to business outcomes.

Domain & Collaboration

• Experience applying data science in digital marketing, customer analytics, or growth analytics is highly desirable but not mandatory.
• Comfortable collaborating with engineering, product, and business teams to build scalable data products and AI‑driven solutions.