Director Data Science

Job Title - Director Data Science
Job Location - Pune, Maharashtra
Must Have Skills - Python, Data Science, ML/AI algorithms, statistical methods, exp in large-scale datasets.
About the Role
Our client is looking for a Director Data Science to build and lead their Data Science and ML capabilities for an enterprise cybersecurity platform.
This role combines deep technical expertise in Data Science, Machine Learning, Analytics, and large-scale data processing with strong engineering and technical leadership. You will work on analyzing billions of events, flows, logs, and other security data to build intelligent capabilities that help customers understand their security posture, identify risks, detect anomalies, and derive actionable insights.
You will own the end-to-end technical direction and delivery of Data Science/ML components, from problem formulation and algorithm development through productionization, scalability, performance, and ongoing improvement.
This is a hands-on leadership role in a fast-moving startup environment, requiring a balance of technical depth, product thinking, execution, and team leadership.
Key Responsibilities
Lead Data Science & ML: Define and drive the technical vision, roadmap, and execution for Data Science, Machine Learning, AI, and advanced analytics capabilities.
Large-Scale Data Analytics: Develop solutions capable of analyzing billions of events, network flows, logs, identities, and security signals at cloud scale.
End-to-End Ownership: Own Data Science/ML components throughout their lifecycle—from problem definition, data exploration and algorithm design to implementation, production deployment, monitoring, and optimization.
Build Production-Grade Solutions: Ensure developed solutions are scalable, reliable, performant, maintainable, and production-ready, working closely with software engineering and infrastructure teams.
ML & AI: Apply supervised and unsupervised learning, anomaly detection, clustering, classification, graph analytics, statistical methods, and other AI/ML techniques to solve complex cybersecurity problems.
Data & Feature Engineering: Drive approaches for data preparation, feature engineering, model development, evaluation, and experimentation across large and diverse security datasets.
Cybersecurity Analytics: Develop analytical and ML-based capabilities for use cases such as threat detection, behavioral analysis, identity and access analytics, risk assessment, security posture, and anomaly detection.
Technical Leadership: Make key architectural and technical decisions related to Data Science/ML systems and establish engineering and development best practices for the function.
Cross-Functional Collaboration: Partner closely with Engineering, QA, UI, DevOps, IT/Ops, Product Management, and senior management to take solutions from concept to production.
Product Partnership: Work with Product Management and domain experts to translate customer and business problems into scalable data-driven solutions.
Team Leadership & Mentorship: Build, mentor, and grow a strong Data Science/ML team while maintaining a high technical bar and fostering a culture of ownership, experimentation, and engineering excellence.
Innovation: Evaluate and adopt relevant advances in AI/ML, GenAI, graph analytics, and data science where they can create meaningful product value.
Startup Execution: Operate effectively in a fast-paced startup environment, balancing long-term technical investments with rapid product delivery.
Required Qualifications
15+ years of hands-on experience in Data Science, Machine Learning, AI, Analytics, or a closely related field, with significant experience building production-grade solutions.
Strong hands-on expertise in Data Science, ML/AI techniques, algorithms, and statistical methods.
Deep understanding of supervised and unsupervised learning, including classification, clustering, anomaly detection, dimensionality reduction, and related techniques.
Strong programming experience in Python and hands-on experience with relevant data science and ML frameworks such as:
NumPy
Pandas
Scikit-learn
NetworkX
TensorFlow / Keras or equivalent ML frameworks
Strong understanding of data processing, feature engineering, model evaluation, experimentation, and productionization of ML models.
Experience working with large-scale datasets and distributed/cloud environments.
Strong understanding of software engineering principles, including architecture, scalability, reliability, performance, testing, CI/CD, and production operations.
Strong problem-solving, analytical, and quantitative skills.
Demonstrated ability to take ambiguous problems and drive them from problem definition to production solution.
Strong communication and collaboration skills, with the ability to work effectively across engineering, product, and business teams.
Bachelor's, Master's, or PhD in Computer Science, Data Science, Mathematics, Statistics, Engineering, or equivalent practical experience.
Good to Have
Experience in cybersecurity, network security, identity security, or enterprise security analytics.
Experience analyzing network traffic, flows, security events, audit logs, identity data, or telemetry.
Experience with event/log analytics platforms, such as ELK/OpenSearch or equivalent.
Experience with graph analytics and graph-based ML, particularly using NetworkX or similar technologies.
Knowledge of data query and processing technologies such as SQL, MongoDB, or equivalent.
Experience with distributed data processing technologies such as Spark or similar frameworks.
Experience building ML/analytics capabilities that operate at cloud scale and high data volumes.
Experience with MLOps, model monitoring, experimentation, and model lifecycle management.
Experience applying LLMs/GenAI to cybersecurity or enterprise data analytics use cases.

Work arrangement
No