Job description

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 reliability, governance, customer impact, cost, and long-term maintainability. • Self-directed, results-oriented, and able to manage multiple complex assignments with limited technical guidance. • Commitment to continuous learning and awareness of emerging data science, machine learning, and generative AI trends. MAJOR JOB RESPONSIBILITIES • Partner with Enterprise Data & Customer Analytics leaders and business stakeholders to identify, prioritize, and refine use cases that improve customer experiences, operational performance, decision-making, and enterprise value. • Develop production-quality solutions using Databricks, Python, PySpark, SQL, and related cloud technologies, ensuring that analytical work is scalable, reusable, testable, documented, and supportable. • Design and evaluate solutions that interact with large language models, including prompt-based applications, retrieval-augmented generation, grounded question answering, summarization, classification, and agentic or workflow-oriented patterns where appropriate. • Establish rigorous experimentation and evaluation methods to validate assumptions, compare alternatives, quantify model performance, and determine whether solutions are ready for production use. • Define and promote data science, MLOps, LLMOps, responsible AI, model documentation, and monitoring standards in alignment with enterprise architecture, governance, privacy, security, and risk requirements. • Monitor model and solution performance, identify drift or degradation, perform root-cause analysis, and recommend retraining, redesign, retirement, or other corrective actions. • Collaborate with data engineers, architects, analysts, application teams, cybersecurity, governance teams, and business stakeholders to ensure required data and platforms are trusted, accessible, secure, and fit for purpose. • Create reusable code, features, frameworks, reference patterns, evaluation assets, and documentation that accelerate future analytics and AI delivery. • Communicate methods, findings, limitations, risks, and recommendations through executive presentations, business reports, technical documentation, white papers, and demonstrations. • Provide technical leadership and mentorship to data science and analytics practitioners through design reviews, code reviews, paired problem-solving, training, and communities of practice. • Evaluate emerging methods, tools, and research; conduct targeted prototypes; and recommend adoption when capabilities demonstrate clear business relevance and responsible use. • Build internal and external partnerships to share practices, monitor industry developments, and strengthen the organization's data science and AI capabilities. DECISION-MAKING AND TASK COMPLEXITY This role operates with a high degree of technical autonomy and is expected to make significant decisions regarding analytical methods, model design, experimentation, evaluation, and implementation. Assignments are varied, ambiguous, and highly complex, often requiring original problem-solving, advanced quantitative methods, and coordination across multiple business and technology areas. The role seeks leadership alignment for material business, risk, funding, or policy decisions and follows all applicable company standards and controls. WORK DIRECTION AND TECHNICAL LEADERSHIP This is a principal-level individual contributor role. The position may provide work direction and technical leadership to cross-functional teams, project resources, consultants, and less-experienced practitioners. Responsibilities may include setting technical approaches, reviewing analytical designs and code, mentoring others, and guiding delivery quality. Unless separately assigned, the role does not have direct authority for hiring, termination, performance appraisal, or discipline. CORE BEHAVIORAL ATTRIBUTES The successful candidate will demonstrate understanding and application of the Southern Company behaviors: Unquestionable Trust, Superior Performance, and Total Commitment. • Acts ethically and protects customer, employee, and company data. • Delivers results with accountability, sound judgment, and attention to quality. • Thinks strategically and maintains an enterprise perspective when designing sustainable solutions. • Innovates responsibly and tests assumptions with evidence. • Builds constructive partnerships and works effectively across business and technology teams. • Communicates candidly, clearly, and respectfully. • Develops others through coaching, knowledge sharing, and technical mentorship. • Adapts to changing priorities and acts with appropriate speed and decisiveness. • Demonstrates commitment to continuous learning, inclusion, and safe work practices. PREFERRED DIFFERENTIATORS • Experience delivering customer analytics use cases such as segmentation, propensity, personalization, forecasting, customer journey analysis, service optimization, or next-best-action recommendations. • Experience combining enterprise data products with advanced analytics and generative AI capabilities. • Published research, patents, conference presentations, open-source contributions, or recognized thought leadership in data science, machine learning, or artificial intelligence. • Experience establishing reusable analytical frameworks, technical standards, or enterprise communities of practice.

Requirements

Must-haves

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.
Demonstrated experience developing, validating, deploying, monitoring, and improving analytical and machine learning models in enterprise environments.
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.
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 reliability, governance, customer impact, cost, and long-term maintainability.
Self-directed, results-oriented, and able to manage multiple complex assignments with limited technical guidance.
Commitment to continuous learning and awareness of emerging data science, machine learning, and generative AI trends.
Develop production-quality solutions using Databricks, Python, PySpark, SQL, and related cloud technologies, ensuring that analytical work is scalable, reusable, testable, documented, and supportable.

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaipythonmlsqlci/cd

Platform: oracle-cloud
Effort: medium