Senior Data Scientist, AI Analytics

Senior Data Scientist, AI AnalyticsThe Senior Data Scientist, AI Analytics develops predictive modeling and generative AI frameworks to solve operational inefficiencies across business domains. This role executes the translation of complex datasets into resilient production machine learning pipelines and algorithmic solutions that enhance customer lifetime value and retention. Key challenges center on standardizing model reproducibility, monitoring algorithmic drift across production environments, and supporting automated compliance workflows. Success requires applying statistical theory to inform business decisions, delivering operational efficiency, and driving continuous technological innovation.What Success Looks Like (Objectives)Partner with operational and engineering leaders to establish machine learning roadmaps that align with enterprise OKRs and product objectivesDesign, deploy, and maintain scalable production machine learning systems for customer segmentation, predictive churn mitigation, and lifetime value optimizationImplement and scale generative AI initiatives by engineering robust LLM-assisted workflows for automated multi-channel transcript analysis and real-time compliance auditingEstablish automated data science pipelines across enterprise cloud infrastructures to support model reproducibility, auditability, and continuous deliveryLeverage AI developer tooling and automated monitoring frameworks to optimize model training efficiency, track pipeline latency, and streamline deployment cyclesTranslate complex model outputs into analytical dashboards and actionable recommendations that inform operational decisions across core business unitsCore Skills, Competencies, and Key Experiences (What you'll bring)Proven capability in designing, deploying, and maintaining production-grade predictive machine learning models, including classification, regression, and time-series forecastingApplied AI proficiency in prompt engineering, model validation, and deployment of large language models within production environmentsStrong technical capabilities in Python, Scala, and SQL, coupled with hands-on proficiency in machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorchAbility to design and optimize distributed data science pipelines within enterprise cloud platforms such as AWS, Databricks, or SnowflakeDeep expertise in experimental design, causal inference, and rigorous A/B testing methodologies to validate algorithmic impact against critical business metricsCollaborative problem-solving and communication skills to convey statistical findings and technical roadmaps to cross-functional stakeholders and leadershipCandidates must be willing to participate in at least one in-person interview.Minimum Requirements:Minimum Education: Bachelor's Degree in Statistics, Machine Learning, Computer Science, Engineering, Mathematics, Physics, or a related quantitative fieldMinimum Experience: 4 years of experience in data science and applied machine learningRequired Technical Skills: Must have at least 4 years of experience with:Python, Scala, and SQLMachine learning libraries such as Scikit-learn, TensorFlow, or PyTorchCloud-based AI and data platforms such as AWS, Databricks, or SnowflakeUnstructured data processing for natural language processing (NLP) or generative AI workflowsVisa sponsorship not available for this role#LI-JZ2Compensation: $96,250.00/Year - $137,500.00/Year