Engineering Leader - Data Science

About the CompanyWe are seeking a highly experienced and visionary leader for our Data Science team to lead and shape the future of products using Machine Learning and other data backed initiatives.

About the RoleYou will be responsible for driving the strategic direction of our data science practice, overseeing a team of talented data scientists, ML Engineers and delivering innovative solutions to complex business problems. The ideal candidate should have a strong background in data science, exceptional leadership skills, and a proven track record of driving data-driven strategies and insights. This role will report to the VP Engineering for Data.

ResponsibilitiesLeadership: Provide strategic direction and leadership to the data science team comprising about 13-15 individuals (Data Scientists, ML Engineers, MLOps Engineers), guiding them in delivering high-quality and innovative solutions. Collaborate with Product Management, Engineering, and Program Management to define the data science vision and roadmap aligned with business goals. Stay abreast of the latest advancements in data science, machine learning, and artificial intelligence, and evaluate their potential impact on business operations.Team Management: Lead a high-performing data science team, including data scientists, machine learning engineers, and MLOps.

Foster a collaborative and inclusive team culture, promote professional growth, and mentor team members to enhance their skills and capabilities. Set clear goals and performance expectations, engage the team in regular conversations and ensure the team's work aligns with the organization's vision and objectives.Project Management: Oversee end-to-end execution of data science projects, from problem formulation, model deployment, tuning, and operating them to deliver value to the business. The candidate should be able to break multi-quarter projects into fortnightly milestones, and come up with a traceable Project plan.

Drive the Quarterly planning exercise to arrive at the right OKRs and projects.Collaboration: Collaborate with cross-functional teams, including engineering, product management, analytics, and program management, to identify opportunities and deliver data-driven insights and solutions. Drive the integration of data science capabilities into various business processes and systems. This role requires collaboration with multiple stakeholders, and the candidate should be comfortable managing expectations across the board.Technical Expertise: Stay abreast of the latest advancements and trends in data science, machine learning, and AI technologies.

Provide technical guidance and expertise to the team, ensuring best practices in data analysis, model development, and evaluation.Strategy and Innovation: Work closely with the executive team to define the data science strategy and identify opportunities for leveraging data to drive innovation and create business value. Evaluate emerging technologies and techniques to enhance the team's capabilities and drive continuous improvement.Data Strategy and Governance: Collaborate with stakeholders to define data strategy, including data acquisition, management, and governance practices. Establish standards and best practices for data collection, storage, quality, and privacy, ensuring compliance with relevant regulations.

Identify and implement data infrastructure and technology requirements to support data science initiatives.Performance Evaluation and Reporting: Establish metrics and key performance indicators (KPIs) to track the team's performance and measure the impact of data science initiatives.Continuous Improvement and Innovation: Identify emerging trends, technologies, and methodologies in the field of data science and champion their adoption within the organization. Promote a culture of continuous improvement and innovation, encouraging experimentation, exploring new techniques, and driving efficiency gains. Establish and monitor metrics and KPIs to measure the impact and effectiveness of data science initiatives.

QualificationsMaster's or Ph.D. in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics, or related disciplines.Minimum of 12-15 years of experience in data science, machine learning with a track record of delivering data-driven solutions in a leadership capacity.Proven experience in leading and managing a team of data scientists and driving successful project delivery. Strong ability to inspire, motivate, and mentor team members.Excellent communication skills, with the ability to effectively communicate complex technical concepts to both technical and non-technical stakeholders. Demonstrated ability to collaborate and build relationships with diverse teams.Strong business understanding and the ability to translate business objectives into data science projects and deliverables.

Experience in aligning data science initiatives with organizational goals and driving measurable outcomes.Ability to think strategically and provide thought leadership in data science and AI. Proven experience in developing data science strategies and roadmaps that support.

Required SkillsCore ML/DS Technical Skills Machine Learning & Statistical Foundations (Must Have)Deep understanding of ML algorithms (supervised, unsupervised, reinforcement learning)Experience with deep learning frameworks (TensorFlow, PyTorch)Strong grasp of statistical methods, A/B testing, and experimentation frameworksTime series forecasting, Anomaly Detection Techniques, Multi-arm bandit problems.Strong Command on Python, and either of Java/C++/Scala.Exposure to Domain-Specific ML Applications (Must Have)Routing OptimizationFraud detection and prevention systemsRisk modeling and credit scoringRecommendation systemsNLP for customer support automation and document processingReal-time ML inference and streaming ML pipelinesPlatform & Infrastructure ML Engineering & MLOps (Must Have)ML model lifecycle management (training, versioning, deployment, monitoring)Feature engineering platforms and feature storesModel serving infrastructure (batch and real-time)ML observability, drift detection, and model performance monitoringCI/CD for ML systems, MLFlow, SageMaker, Airflow.Data InfrastructureDistributed computing frameworks (Spark, PySpark, Flink) (Must Have)Data warehousing solutions (Redshift, Snowflake, BigQuery)Streaming platforms (Kafka, Kinesis) (Optional)Understanding of data lake architectures (Iceberg, Delta Lake) (Optional)Engineering FundamentalsStrong programming skills (Python, Scala, or Java)System design for high-scale, low-latency applicationsCloud platforms (AWS/GCP/Azure) and containerization