Data Science Manager

Key Responsibilities

1. Pre-Sales & Client Engagement

1. Collaborate with the sales and business development teams to identify client needs and formulate AI/ML solutions.
2. Present technical concepts, project proposals, and proof-of-concepts (POCs) to prospects and clients.
3. Translate complex client requirements into actionable project scopes, estimates, and technical proposals.

1. Leadership & Team Management

1. Provide direction, mentorship, and performance feedback to a team of data scientists and ML engineers.
2. Establish best practices in solution design, code reviews, model validation, and production deployment.
3. Drive the strategic roadmap for AI initiatives, ensuring alignment with organizational goals and market trends.

1. Classical Machine Learning & Statistical Modeling

1. Apply classical machine learning techniques (e. g. , regression, clustering, decision trees, ensemble methods) to solve diverse business problems.
2. Design and optimize data pipelines, feature engineering processes, and model selection strategies.
3. Ensure robust model evaluation, tuning, and performance monitoring in production environments.

1. Deep Learning & Generative AI

1. Develop and maintain deep learning models using frameworks such as TensorFlow or PyTorch for tasks like computer vision, NLP, or recommendation systems.
2. Explore and build solutions leveraging generative AI (GANs, VAEs, or transformer-based architectures) for innovative product features and services.
3. Champion research and experimentation with state-of-the-art AI models, staying ahead of industry advances.

1. Project Delivery & MLOps

1. Lead end-to-end ML project lifecycles, from data exploration and model development to deployment and post-launch maintenance.
2. Implement MLOps best practices (CI/CD, containerization, model versioning) on cloud or on-premise infrastructures.
3. Collaborate with DevOps and engineering teams to integrate ML solutions seamlessly into existing systems.

1. Stakeholder Management & Communication

1. Serve as a key technical advisor to executive leadership, product managers, and client teams.
2. Communicate complex AI/ML findings in clear, actionable terms to both technical and non-technical audiences.
3. Advocate data-driven decision-making and foster a culture of innovation within the organization.

Required Qualifications

• Education & Experience

1. Masters or PhD in Computer Science, Data Science, Engineering, or a related field is preferred.
2. 12+ years of relevant industry experience in data science or ML engineering, with 5+ years in a leadership or management capacity.

• Technical Expertise

1. Pre-Sales : Demonstrated experience in client-facing roles, solutioning, and proposal development.
2. Classical ML : Skilled in traditional algorithms (regression, classification, clustering, etc. ) and statistical methods.
3. Deep Learning : Hands-on expertise with frameworks (e. g. , TensorFlow, PyTorch) for CNNs, RNNs, transformer architectures, etc.
4. Generative AI : Practical exposure to GANs, VAEs, or large language models, with a track record of building generative models.
5. MLOps : Familiarity with CI/CD pipelines, Docker/Kubernetes, and cloud platforms (AWS, Azure, GCP).

• Leadership & Communication

1. Proven ability to mentor and lead data science/ML engineering teams to meet project goals.
2. Exceptional communication skills for presenting to clients, stakeholders, and executive leadership.

Mlops, Presale, Data Science, Ml