Principal Data Scientist

Do you have the following skills, experience and drive to succeed in this role Find out below.

ROLE

• Leads the design and implementation of advanced analytics methodologies to address complex business challenges.
• Oversees and executes end-to-end analyses, including data acquisition, processing, exploratory analysis, modeling, validation, and presentation of actionable insights.
• Drives enterprise data enrichment strategies by integrating internal and external data sources to generate deeper insights.
• Defines and implements data collection enhancements to ensure relevance and quality for advanced analytical systems.
• Develops and deploys cutting-edge automated anomaly detection systems, ensuring real-time monitoring and model performance tracking.
• Acts as a strategic partner to senior stakeholders across the organization to understand complex business needs, frame problems, and identify opportunities for leveraging data to achieve measurable outcomes.
• Establishes and advocates for robust data structures, metrics, and governance standards to enable scalable and reliable analytics solutions.
• Provides mentorship and peer reviews for the work of other data scientists and analytics professionals to maintain high-quality standards.
• Leads the application of AI, machine learning (ML), and advanced analytics approaches to optimize revenue, reduce costs, and enhance customer / member experiences.
• Designs, builds, and deploys AI / ML / DL models at scale, focusing on driving innovation and achieving business objectives.
• Leads the development of cutting-edge generative AI (GenAI) solutions using large language models (LLMs).
• Frames and prioritizes hypotheses for solving strategic business problems, leveraging robust statistical and ML methods.
• Continuously evaluates and improves the effectiveness, accuracy, and scalability of AI / ML models, incorporating new data sources and methodologies as needed.
• Communicates analytical findings effectively through advanced storytelling and visualization tailored to executive-level audiences.
• Designs and oversees rigorous A / B testing frameworks for model evaluation and business experimentation, providing actionable recommendations.
• Leads back-testing initiatives to validate model performance and improve forecasting accuracy.
• Drives the development and adoption of enhanced AI / MLOps frameworks, including experiment tracking, CI / CD pipelines, and scalable deployment.
• Leads cross-functional collaborations to ensure data readiness, including advanced data wrangling, feature engineering, and preparation.

REQUIRED QUALIFICATIONS

• 10 years of experience applying advanced AI / ML / DL methodologies to solve high-impact business problems, with a proven track record of delivering measurable business value.
• Demonstrated expertise in designing and deploying ML models at scale in production environments.
• Advanced proficiency in SQL, PySpark, Python, and frameworks for machine learning and deep learning.
• Expertise in data visualization tools (e.g., Power BI, Tableau) to effectively communicate insights.
• In-depth experience with machine learning platforms such as Databricks, GCP, Azure, and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Advanced knowledge of AI / ML methods across retail, supply chain, pricing, and inventory optimization, including deep learning and neural networks.
• Proficiency in MLOps practices, including CI / CD for ML pipelines, experiment tracking (e.g., MLflow), and model lifecycle management.
• Demonstrated ability to lead initiatives that integrate diverse data types on cloud platforms to deliver innovative solutions.
• Strong knowledge of Agile methodologies and experience working within Agile teams.
• Proven ability to translate complex technical concepts into actionable business strategies, with exceptional communication and storytelling skills.

RECOMMENDED QUALIFICATIONS

• 12 years of experience, with a Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field (PhD preferred).
• Experience designing scalable, repeatable analytics solutions in cloud-based environments like Databricks or similar platforms.
• Background in retail or consumer goods industries, with a focus on applying advanced analytics to business operations.
• Strong software development skills and familiarity with modern data engineering practices.
• Demonstrated passion for driving business value through experimentation, A / B testing, and analytical curiosity.

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