Principal Data Scientist

Job-:
Information Technology

Location:

Atlanta, GA, United States

Schedule:

Full time Posted:
Closes: JOB SUMMARY: The Principal Data Scientist for Enterprise Data & Customer Analytics provides senior technical leadership in the design, development, deployment, and responsible use of advanced analytics, machine learning, and generative AI solutions. This role transforms complex enterprise and customer data into actionable intelligence, production-ready models, and reusable analytical capabilities that improve customer experiences, business decisions, and operational outcomes.

The position combines deep data science and software engineering expertise with business acumen, intellectual curiosity, and enterprise influence. The Principal Data Scientist works across business and technology teams to identify high-value opportunities, establish technical standards, guide model development, and communicate complex findings in a clear and decision-oriented manner.

JOB REQUIREMENTS Education and Experience Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Artificial Intelligence, Machine Learning, or a closely related quantitative field is required. Must have a minimum of 6 years of experience working with large, diverse, and sensitive datasets and translating analytical results into measurable business value.

Extensive progressive experience applying data science, machine learning, advanced analytics, or artificial intelligence to complex business problems, including experience serving as a senior technical lead or principal-level individual contributor.

Demonstrated experience developing, validating, deploying, monitoring, and improving analytical and machine learning models in enterprise environments.

Experience in the energy, utility, customer analytics, or other highly regulated industry is preferred.

Technical Knowledge and Skills Advanced proficiency in Python and PySpark for data preparation, exploratory analysis, feature engineering, statistical analysis, machine learning, and production-scale model development.

Strong hands-on knowledge of Databricks, including collaborative development, distributed computing with Apache Spark, ML lifecycle practices, model tracking, governed data access, and deployment patterns.

Advanced knowledge of statistical methods, experimental design, causal reasoning, predictive modeling, forecasting, classification, clustering, anomaly detection, and model evaluation.

Demonstrated knowledge of supervised and unsupervised learning, natural language processing, neural networks, ensemble methods, recommendation techniques, and other modern machine learning approaches.

Strong knowledge of large language models, including prompt design, model interaction patterns, retrieval-augmented generation, embeddings, vector search, evaluation, grounding, guardrails, and responsible AI considerations.

Experience developing or adapting models through techniques such as fine-tuning, parameter-efficient tuning, feature engineering, hyperparameter optimization, and systematic experimentation.

Ability to design reliable model evaluation approaches for predictive and generative AI solutions, including quality, accuracy, bias, explainability, safety, drift, performance, and business-impact measures.

Strong SQL and data engineering skills, including experience integrating structured and unstructured data across enterprise platforms and building repeatable analytical pipelines.

Knowledge of cloud-based data and AI platforms, software development practices, version control, automated testing, APIs, CI/CD, and MLOps or LLMOps practices.

Ability to debug and troubleshoot complex data, code, model, and performance issues across the analytical lifecycle.

Working knowledge of data governance, data privacy, security, model risk, ethical AI, and the appropriate handling of customer and enterprise data.

Leadership and Business Skills Ability to identify and shape high-value data science and AI opportunities in partnership with customer, operational, product, and technology stakeholders.

Strong consulting, facilitation, and communication skills, with the ability to translate technical concepts into meaningful business insight for audiences at all levels of the organization.

Demonstrated ability to provide technical direction, establish standards, influence without direct authority, and mentor data scientists, data engineers, analysts, and business partners.

Ability to balance innovation with production…