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Director, ML Pre-Sales Engineering Leader
2 months ago
We are seeking a highly experienced and skilled Director, ML Pre-Sales Engineering to join our team at Genai Works. As a key member of our organization, you will be responsible for leading a team of machine learning solution engineers and driving the development of our pre-sales strategy.
Key Responsibilities- Team Leadership: Lead a team of world-class machine learning solution engineers who serve as our first-line expert educators on Genai Works' platform.
- Pre-Sales Strategy: Develop and execute a pre-sales strategy that aligns with our company's goals and objectives.
- Customer Engagement: Act as a trusted advisor to our customers, helping them to solve complex data science problems using Genai Works' platform.
- Process Improvement: Identify areas for process improvement and implement changes to increase efficiency and effectiveness.
- Training and Development: Teach, train, enable, and upskill our team of machine learning engineers into a team of high-caliber technical sellers aligned with our value-centric sales playbook and process.
- Industry Experience: 7+ years of industry experience in a pre-sales or consulting capacity with 2+ years of direct line management experience (hiring, training, and retaining top talent).
- Technical Expertise: Industry expertise within machine learning, business intelligence, data analytics, cloud computing, and/or SaaS.
- Leadership Skills: Proven ability to lead and manage high-performing teams, with a strong track record of hiring, training, and retaining top talent.
- Communication Skills: Outstanding presentation skills to both technical and executive audiences.
- Education: University degree in computer science, engineering, applied mathematics, or a related field, or equivalent experience.
- Pre-Sales Experience: Experience building and evolving pre-sales playbooks based on Force Management Command of the Message (CoM) Value Selling Framework.
- Scripting and ML Libraries: Fluency with scripting in Python and common ML libraries.
- Production ML Pipelines: Experience with all stages of production ML pipelines, from problem framing and data preparation to model deployment and monitoring.
- Machine Learning Models: Hands-on experience building and implementing machine learning models.
- Business Stakeholder Collaboration: Experience collaborating with business stakeholders to ensure that machine learning solutions deliver successfully on business outcomes.
