Sr Data Scientist

At F5, we strive to bring a better digital world to life.

Our teams empower organizations across the globe to create, secure, and run applications that enhance how we experience our evolving digital world.

We are passionate about cybersecurity, from protecting consumers from fraud to enabling companies to focus on innovation.

Everything we do centers around people.

That means we obsess over how to make the lives of our customers, and their customers, better.

And it means we prioritize a diverse F5 community where each individual can thrive.

Senior Data Scientist Location: Hyderbad, India (F5 Office 3-5 days/week) Reports To: Director of Data Science Team: Data & Insights Compensation: Competitive salary plus benefits Role Overview Join F5's Data & Insights team as a Senior Data Scientist and drive high-impact outcomes through data.

You'll own end-to-end modeling projects — from problem framing and stakeholder alignment through deployment and communication of results.

Leveraging advanced statistical methods, machine learning, and large language models (LLMs), you'll create and scale data products that drive decision-making, boost revenue, and streamline operations.

As a senior member of the team, you'll guide technical direction, elevate team capabilities, and serve as a trusted partner to stakeholders across the organization.

Key Responsibilities End-to-End Project Ownership Independently initiate, scope, execute, and deliver end-to-end modeling projects — from exploratory analysis and feature engineering through model deployment, monitoring, and results communication.

Define project milestones, make responsible commitments, and consistently deliver against them through every phase of the project lifecycle.

Communicate project outcomes, business impact, and strategic recommendations to audiences at all levels, including senior leadership.

Stakeholder Partnership & Project Shaping Lead stakeholder conversations to clarify ambiguous requirements, refine project specifications, and align on measurable business outcomes.

Proactively guide stakeholders through trade-offs, prioritization, and expectation-setting to ensure projects are well-scoped and deliver meaningful value.

Build and maintain trusted relationships with project management, data engineering, data analytics, customer success, marketing, and finance teams.

Data Analysis & Modeling Design, develop, and deploy advanced statistical models and machine learning algorithms to address descriptive, predictive, and prescriptive business needs.

Perform deep exploratory data analysis to uncover trends, patterns, and anomalies in large datasets; translate findings into clear, actionable narratives.

Build predictive and classification models to forecast key metrics, support operational decisions, and guide business strategy.

Design and execute experiments, including A/B tests and causal inference methods, to measure impact and inform strategic decisions.

Technical Leadership & Mentorship Guide team members on optimal approaches for feature creation and refinement, model evaluation and tuning, and reliable, scalable pipeline deployment.

Establish and promote best practices for model development, validation, reproducibility, and production readiness.

Conduct rigorous code reviews and model reviews, raising the quality bar across the Data & Insights team.

AI, Agents & LLM Solutions Lead projects for agent evaluation, prompt effectiveness evaluation, and prompt evolution strategies.

Drive context engineering approaches for grounding LLM solutions in enterprise data, ensuring accuracy, relevance, and safety.

Contribute to the technical specification and architecture of agent-based solutions for internal business problems.

Leverage AI-assisted coding tools responsibly in day-to-day work to improve speed, reliability, repeatability, and quality of results.

Innovation & Continuous Improvement Stay current with advancements in AI tools, agentic frameworks, data science methodologies, machine learning, and causal inference.

Continuously refine models, processes, and team workflows to improve accuracy, efficiency, and business impact.

Identify and champion opportunities to apply new techniques and technologies to existing and emerging business problems.

Qualifications Education Master's degree in Data Science, Statistics, Computer Science, Mathematics, Physics, or a related quantitative field.

PhD is a plus.

Experience 5+ years of professional experience in data science, with a demonstrated track record of independently owning and delivering end-to-end modeling projects that drove measurable business outcomes.

Please provide an IP-respectful portfolio of work examples.

Proven experience managing stakeholder relationships — including scoping projects, navigating ambiguity, setting expectations, and delivering results.

Hands-on experience working with large-scale datasets using distributed computing platforms such as Snowflake and Databricks .

Experience mentoring or guiding other data scientists or analysts on technical best practices.

Technical Skills Expert-level proficiency in SQL and Python .

Skilled in LLM prompt engineering, prompt evaluation methodologies, context engineering, and integrating LLM APIs into production workflows.

Deep expertise with machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn).

Advanced knowledge of feature engineering, feature stores, feature evaluation, and model selection strategies.

Experience with MLflow and end-to-end ML lifecycle management.

Strong understanding of experiment design, A/B testing, and causal inference methodologies.

Experience with containerization and deployment tools (e.g., Docker , Kubernetes ) for productionizing models and data science solutions.

Demonstrated ability to build, optimize, and maintain reliable, scalable ML pipelines.

Proficient with version/source control (e.g., Git), CI/CD workflows, and AI coding assistants.

Proven ability to evaluate, optimi