• Develop and implement data science strategies to drive business growth.
• Collaborate with cross-functional teams to identify opportunities for data-driven insights and solutions.
• Lead the development of machine learning models using Python and other relevant tools.
• Conduct regular model maintenance and optimization to ensure accuracy and efficiency.
• Stay up-to-date with emerging trends and best practices in data science
• Build cutting-edge capabilities utilizing machine learning and data science (e.g., large language models, computer vision models, voice models etc.)
• Mentor, guide, coach, coordinate training opportunities and support individual growth and technical skills across the engineering teams.
• Enable continuous learning environment to keep abreast of industry trends. Partner with research organizations and explore innovation opportunities.
• Leverage industry best practices and tools to continually improve teams' ability to build, operate and maintain products.
• Ensure that technical solutions are in line with established company strategy, standards in respect to architecture, security, corporate governance, coding standards, monitoring, logging, unit test, and service enablement.
• As a director of machine learning, you will be responsible for overseeing the ML strategy, vision, and roadmap of your organization. You will also be leading and mentoring a team of ML professionals, and collaborating with other teams and departments.
• You are expected to grow a strong bench of ML/AI leaders to help Sprinklr further expand its ML/AI footprint in company.

What Makes You Qualified

• Bachelor's or master's degree in computer science, mathematics, statistics, or related field.
• 8+ years of experience managing Machine Learning teams.
• 10+ years of experience designing, building, deploying, testing, maintaining, monitoring, and owning scalable, resilient, and distributed machine learning algorithms and systems.
• Proficient in Python, GEN AI, ML, Natural Language Processing (NLP), Deep Learning (DL), and Graph Analytics.
• Experience in directly working with cloud providers such as AWS, Azure, GCP is a plus.
• Strong problem-solving skills and ability to work collaboratively in a team environment.
• Proficiency in operating machine learning solutions at scale, covering the end-to-end ML workflow.
• Familiarity with real-world ML systems (configuration, data collection, data verification, feature extraction, resource and process management, analytics, training, serving, validation, experimentation, monitoring).
• Obsession for service observability, instrumentation, monitoring, and alerting.