๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ด๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ญ๐ฒ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ด๐ฌ-๐ญ๐ฒ๐ฌ ๐๐ฃ๐)
Experience: 16+ yrs
Location: Bengaluru
We are seeking an accomplishedย Director of Applied Scienceย to lead advanced machine learning and artificial intelligence initiatives that drive innovation, improve business outcomes, and expand the capabilities of data-driven products and services. The ideal candidate will combine deep technical expertise, strategic leadership, and a strong track record of delivering production-grade AI/ML solutions.
This role requires an experienced leader who can define the applied science vision, guide research and experimentation, and translate scientific advancements into scalable business applications. You will lead applied scientists and machine learning engineers, establish technical standards, and collaborate with Product, Engineering, and Data teams to deliver measurable business impact.
Requirements
Key Responsibilities
- Lead and develop a team of applied scientists and machine learning engineers building advanced models, algorithms, and AI-powered solutions.
- Define the technical vision, research strategy, and roadmap for applied science initiatives in alignment with business priorities.
- Identify opportunities to apply machine learning, deep learning, and advanced analytics to complex business problems.
- Oversee experimentation, model development, evaluation, validation, and deployment into production environments.
- Guide the development of supervised, unsupervised, and deep learning solutions for real-world applications.
- Partner with Product, Engineering, and Data teams to integrate ML models into scalable, reliable, and high-performance systems.
- Establish best practices for scientific experimentation, statistical validation, reproducibility, model monitoring, and continuous improvement.
- Champion responsible AI principles, including model transparency, fairness, reliability, and appropriate risk management.
- Evaluate emerging research, algorithms, and technologies to identify opportunities for innovation and competitive advantage.
- Ensure projects are delivered within agreed timelines and scope while maintaining technical quality and business relevance.
- Monitor project progress, manage technical risks, remove blockers, and prioritise resources across multiple initiatives.
- Mentor scientists and engineering leaders, strengthen technical capabilities, and foster a culture of innovation and continuous learning.
- Define success metrics and evaluate the business impact of applied science initiatives.
- Represent the applied science function in executive discussions, strategic planning, and technology roadmap decisions.
What Makes You a Great Fit
- 16+ years of experienceย in applied science, machine learning, artificial intelligence, or related technical fields.
- At leastย 3 years of experience in a management or technical leadership role, leading scientific or ML engineering teams.
- A PhD or Master's degree in Machine Learning, Computer Science, Statistics, or a related discipline.
- Proven experience building, deploying, and scaling machine learning models in production systems.
- Deep understanding of supervised learning, unsupervised learning, deep learning, and advanced modelling techniques.
- Strong programming skills inย Pythonย and hands-on proficiency with frameworks such asย PyTorch or TensorFlow.
- Experience leading research-driven initiatives and translating scientific findings into practical product or business solutions.
- Strong understanding of model evaluation, experimentation, statistical validation, and production ML lifecycle practices.
- Demonstrated ability to define technical strategy, manage complex projects, and deliver measurable business outcomes.
- Excellent people leadership, mentoring, and team development capabilities.
- Strong communication and stakeholder-management skills, with the ability to engage technical teams and executive leadership.
- A strategic, analytical, and innovation-oriented mindset with a commitment to scientific rigour and responsible AI.