AI Practitioner

Salt are currently looking for an AI practitioner to work on a three month contract based out of London.
This will involve delivering a high quality and innovative data driven solution to our client’s needs. This will include, working within a team on the clients site: understanding the client’s pain, designing an analytical approach, implementing a solution, ensuring it is of high quality, and leading and mentoring junior team member.

You will also be actively involved with business development (the sales process).

Be part of establishing the AI practice within the Advanced Analytics and Data Science group. Identify opportunities where the tools and methodologies being developed as part of the research into AI could be used to solve “real life” problems our customers are facing.
Key responsibilities:

• Work at a client site as a member of an experienced onsite consulting team developing and applying advanced analytics and data Science methodologies.
• Prepare presentations and reports on findings as well as recommend next steps to ensure successful project completion and to help team penetrate client accounts
• Outline and document methodological approaches and provide support in the development of solutions and preparation of RFP responses
• Consistently deliver high-quality service as well as consultative & business development support to clients
• Seek and develop innovative methods and processes and keep up to date on latest trends, tools and advancements in the area of analytics and data
• Take active part in responding to RFIs and RFPs – support the proactive and reactive sales process by providing the advanced analytics aspect of presentations and responses to clients.
• Lead and mentor junior team members
• Prepare projects action plans while designing timelines and developing risk mitigation plans

Your profile:

• Proven, deep analytic skillset with experience in AI tools and techniques. For example (not exhaustive):
  • Knowledge Base design and implementation (Graph Data Bases)
  • Self-Learning Algorithms and Reinforcement Learning
  • Logical Induction
  • Case Bases Reasoning
  • TensorFlow (Deep leaning ++++)
  • Cognitive Machine Learning
  • Genetic Algorithms and Evolutionary Computing
  • Probabilistic Graphical Models (e.g. Belief Networks)

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