Data Science Architect Offline

We are looking for a Data Science Architect or Data Architect with a strong background in Machine Learning (ML) and Artificial Intelligence (AI) to participate in presales activities.

Responsibilities for a Data Science Architect/ Data Architect in Presales:

Client Engagement: Act as a technical liaison between your organization and potential clients. Engage with clients to understand their business challenges and goals.

Solution Design: Collaborate with the sales team to design data-driven solutions that leverage ML and AI to address client needs. This includes creating customized proposals and presentations.

Technical Expertise: Provide technical expertise and guidance to clients, explaining how ML and AI can solve their specific problems and add value to their operations.

Prototyping: Develop prototypes or proof-of-concept projects to showcase the feasibility and effectiveness of your proposed solutions.

Requirements Gathering: Work closely with clients to gather detailed requirements, ensuring that the proposed solutions align with their expectations.

Cost Estimation: Provide cost estimates and timelines for implementing the proposed solutions.

Communication: Effectively communicate technical concepts to both technical and non-technical stakeholders during the presales process.

Qualifications for a Presales Data Science Architect/ Data Architect:

Strong ML/AI Background: Extensive experience in machine learning and artificial intelligence, including a track record of successfully implementing ML/AI projects.

Data Architecture Skills: Proficiency in data architecture and database management, as well as the ability to design data solutions that support ML/AI initiatives.

Technical Proficiency: Strong programming skills, especially in languages commonly used for data science and AI (e.g., Python, R).

Industry Knowledge: Understanding of the industry or domain in which your clients operate, allowing you to tailor solutions to their specific needs.

Communication Skills: Excellent communication and presentation skills to effectively convey technical concepts to diverse audiences.

Problem-Solving: Strong problem-solving abilities and the capacity to think creatively to address client challenges.

Sales Acumen: A solid understanding of the sales process and the ability to work closely with the sales team to meet revenue targets.

Team Collaboration: The capacity to collaborate with cross-functional teams, including sales, marketing, and technical teams, to develop winning proposals.

Client-Centric Approach: A focus on understanding and meeting the needs of potential clients.