Senior Hiring - DS

·       Client Relationship & Stakeholder Management: Own and strengthen relationships with key client stakeholders, including senior business leaders, technology leaders, and data science leadership. Build trust through consistent delivery, proactive communication, and a strong understanding of client priorities.

·       Large Account Management: Manage data science engagements for large clients, ensuring sustained delivery excellence, customer satisfaction, and long-term engagement growth. Navigate complex client organizations to identify opportunities, align stakeholders, and build strategic partnerships.

·       Customer-Facing Leadership: Act as the primary data science representative in client discussions, steering committee meetings, business reviews, and executive presentations. Communicate complex technical concepts, model outcomes, delivery progress, and strategic recommendations effectively to senior management.

·       Data Science Practice Building: Build, scale, and strengthen data science teams and capabilities aligned with client requirements. Drive talent development, technical excellence, knowledge sharing, and the adoption of industry best practices.

·       Data Science Operations & Delivery: Lead end-to-end data science operations for large accounts, covering delivery planning, resource allocation, capacity management, governance, quality assurance, risk management, and performance tracking.

·       Technical Leadership & Model Reviews: Provide deep technical oversight across machine learning and statistical modelling initiatives. Review modelling approaches, challenge assumptions, assess model performance, and guide teams in developing robust, scalable, and business-aligned solutions.

·       Strategic Planning & Thought Leadership: Partner with clients to define data science roadmaps, identify new analytical opportunities, prioritize high-impact initiatives, and establish scalable operating models for data science delivery.

·       Account Growth & Opportunity Identification: Identify opportunities to expand existing engagements through new use cases, advanced analytics solutions, and broader data science capabilities. Collaborate with business and account leadership to develop proposals and shape future engagements.

·       Operational Excellence & Governance: Establish effective delivery governance, operating rhythms, performance metrics, escalation mechanisms, and continuous improvement practices across the account. Ensure alignment between client expectations, delivery commitments, and business outcomes.

·       Team Leadership & Mentorship: Lead and mentor data scientists, ML engineers, and analytics professionals. Build a high-performing organization with strong technical capabilities, customer orientation, and accountability for delivery outcomes.

Who do we expect ?

●        14+ years of total experience in data science, predictive modeling, or advanced analytics, having spent significant time as a hands-on, code-writing practitioner.

●        5+ years of proven leadership experience managing, growing, and guiding specialized data science teams on production-level projects.

●        Master’s or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a highly quantitative field (preferred).

●        Core Technical Toolkit: Advanced Data Science: Deep expertise in Machine Learning, Deep Learning, Python, TensorFlow, PyTorch, and classical statistical modeling.

o   Modern AI Extensions: Demonstrated proficiency in LLMs, RAG architectures, and fine-tuning pipelines.

o   Engineering & Scale: Strong hands-on experience with cloud platforms (AWS, Azure, GCP, or Snowflake), data pipelining, and operationalizing models via MLOps.

●        Domain Track Record: Prior success in executing production projects within highly analytical sectors.