Data Science Growth Leader
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Job Description
Tiger Analytics is pioneering what AI and analytics can do to solve some of the toughest problems faced by organizations globally. We develop bespoke solutions powered by data and technology for several Fortune 100 companies. We have offices in multiple geographies across the US, UK, India, Australia, Canada, Mexico, and Singapore, and a substantial remote global workforce.
We are seeking a dynamic and experienced Data Science Growth Leader to join our team. This role will co-own business development and growth initiatives from a technical perspective, designing strategy & supporting the execution of GTM efforts for our end-to-end enterprise-grade data science technology capabilities & solutions in AI, ML & AI Engineering across industries & domains. The ideal candidate will have a strong focus on delivering business outcomes for our customers using Data Science and possess a deep & fundamental understanding of DS methodologies and AI technology from experience in successfully delivering such business outcomes in their own careers.
Key Responsibilities
Business Development & Growth:
- Identify and pursue new business opportunities in the AI & ML space.
- Develop and execute strategies to expand our client base and market presence.
- Build and maintain strong relationships with key stakeholders and clients.
- Collaborate with sales and marketing teams to create compelling value propositions and proposals.
Technical Leadership:
- Lead the design, development, and deployment of enterprise-level data science solutions.
- Ensure solutions are scalable, robust, and aligned with business objectives.
- Stay abreast of the latest advancements in AI & ML technologies and integrate them into our offerings.
- Mentor and guide a team of data scientists and engineers to achieve project goals.
Strategic Planning:
- Develop and implement a strategic roadmap for data science initiatives.
- Align data science projects with overall business strategy and client needs.
- Drive innovation and continuous improvement in data science methodologies and practices.
Client Engagement:
- Work closely with clients to understand their business challenges and requirements.
- Translate client needs into actionable data science solutions.
- Present findings, insights, and recommendations to clients in a clear and compelling manner.