Operate as a senior individual contributor, owning complex data science initiatives end‑to‑end—from problem framing and exploratory analysis through model development, validation, and production support.
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Apply strong foundations in statistics and classical machine learning (e.g., regression, classification, tree‑based models, ensembles, time‑series) to solve real‑world business problems.
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Design, develop, and evaluate predictive and analytical models using rigorous statistical and ML techniques.
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Perform feature engineering, model selection, tuning, and performance evaluation, clearly articulating assumptions and tradeoffs.
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Build and apply advanced machine learning and deep learning models where appropriate, using modern frameworks such as PyTorch and/or TensorFlow.
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Write clean, efficient, and production‑quality code, following strong software engineering and data science best practices.
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Partner closely with data engineering and platform teams to ensure models are scalable, reliable, and production‑ready.
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Translate ambiguous business problems into well‑structured analytical approaches and actionable insights.
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Communicate complex analytical results clearly to both technical and non‑technical stakeholders, focusing on business impact.
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Provide technical guidance and informal mentorship to junior data scientists, helping raise the overall quality of data science practice.
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Education:
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11+ years of experience in product management, AI/digital product development, analytics, or data‑driven platform roles.
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Demonstrated experience working on AI‑enabled or advanced analytics products, with a strong understanding of where AI adds real business value and how it is operationalized.
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Strong hands‑on experience with AI experimentation and iterative product development, including hypothesis‑driven experimentation, rapid prototyping, A/B testing, pilots/POCs, and data‑backed decision making.
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Proven ability to design and run experiments to validate AI use cases, assess model and feature effectiveness, measure impact, and inform scale‑up or pivot decisions.
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Proven ability to operate as a senior advisor or product thought leader, influencing without formal authority across product, engineering, and architecture stakeholders.
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Experience collaborating deeply with engineering and platform teams, participating in architecture discussions, design trade‑offs, and build‑vs‑buy decisions.
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Strong background working across the full product lifecycle—from problem discovery and value definition through technical design, delivery, adoption, and measurement.
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Experience partnering closely with US‑based product and technology leaders in a global, matrixed environment.
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Skills:
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Strong foundation in probability, statistics, and classical machine learning algorithms.
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Advanced proficiency in Python and SQL.
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Hands‑on experience with ML libraries such as scikit‑learn, and deep learning frameworks such as PyTorch and/or TensorFlow.
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Solid understanding of the end‑to‑end data science and ML lifecycle, including development, validation, deployment, and monitoring.
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Familiarity with cloud or distributed computing environments and production ML concepts.
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Strong analytical thinking and problem‑solving skills.
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Ability to balance rigor with pragmatism—choosing the right level of model sophistication for the problem.
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Clear, concise communication skills with the ability to influence without formal authority.
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Work Hours:
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About Evernorth Health Services
Evernorth Health Services, a division of The Cigna Group, creates pharmacy, care and benefit solutions to improve health and increase vitality. We relentlessly innovate to make the prediction, prevention and treatment of illness and disease more accessible to millions of people. Join us in driving growth and improving lives.
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