Forward Deployed Data Scientist

Forward Deployed Data Scientist About Arkham Arkham is a Data & AI Platform—a suite of powerful tools designed to help you unify your data and use the best Machine Learning and Generative AI models to solve your most complex operational challenges. Today, industry leaders like Circle K, Mexico Infrastructure Partners, and Televisa Editorial rely on our platform to simplify access to data and insights, automate complex processes, and optimize operations. With our platform and implementation service, our customers save time, reduce costs, and build a strong foundation for lasting Data and AI transformation. About the Role Our implementation teams consist of two key roles: the Forward Deployed Analytics Engineer and the Forward Deployed Data Scientist. These two roles work closely together to drive the implementation of Arkham's Data & AI Platform, helping our customers transform their operations in a matter of weeks. As a Forward Deployed Data Scientist, you are responsible for kickstarting our customers' AI transformation journey. Once their Data Platform is set up in Arkham, you will work hand in hand with them to fully solve their first use case by leveraging our platform's AI capabilities. This process typically involves partnering with our customers' BI, finance, or operations teams, deeply understanding the use case they want to enable, and helping them solve it step by step. Example use cases include : Assisting complex reporting or analysis processes with prompt engineering Deploying anomaly detection or forecasting models to optimize operations Configuring our AI Agent capabilities to simplify data exploration and SQL query generation for Data Engineers This phase typically takes 2-4 weeks, and by the end of it, our business champion will have their first use case or primary pain point fully resolved. This results in their "aha" moment, turning them into an advocate for our platform and driving accelerated adoption and new use cases within their operations. Your role bridges the deployment of Machine Learning Models, implementation of Generative AI use cases, and support for customers with Prompt Engineering. You will become an advocate for our Data & AI Platform, managing 3-4 customer implementations at any given time. Core Responsibilities Client‑Focused Solution Design: Collaborate with clients to understand their most critical challenges and develop solution plans leveraging our platform. Solve the Client's First Use Case: Drive the deployment of Arkham's Data & Applications tools to address our clients' initial use cases, including developing data pipelines, implementing advanced analytics, deploying our ML Models, and configuring AI Agents. Iterative Feedback Loops: Engage in rapid iteration with clients, deploying proofs‑of‑concept and refining proposed solutions based on feedback. Lead Client Adoption: Collaborate with clients' IT, Data Engineering, and Analytics teams to ensure effective adoption of our platform. Business Strategy & Insights: Act as a trusted business advisor to clients, identifying further opportunities for Arkham's platform to drive value and influencing broader strategic decisions through data‑driven insights. Ownership of Project Lifecycle: Lead projects from discovery to long‑term follow‑up, ensuring that deployed solutions continue to generate sustained impact for our clients. What We Value Entrepreneurial Mindset: Ability to navigate ambiguity, take calculated risks, and lead bold initiatives in dynamic, fast‑changing environments. Exceptional Communication Skills: Ability to translate complex technical solutions into clear, business‑relevant terms, fostering strong relationships with both clients and internal teams. Adaptability & Resilience: Thrives in challenging environments, with a proven ability to solve complex problems and deliver results. What We Require Educational Background: A Bachelor's degree in a quantitative field such as Science, Statistics, Computer Science, or a similar discipline. Mathematical and Statistical Knowledge: A strong foundation in mathematics, particularly in statistical models and techniques. Experience: 3+ years hands‑on experience as a Data Scientist. Technical Skills: Proficiency with Python and SQL; Experience with forecasting models; Experience with traditional machine learning models, supervised and unsupervised; Experience with Generative AI; Knowledge of AWS cloud platform (Lambda, EC2, SageMaker, etc); Proficiency using software version control tools such as GIT; Experience with data engineering tools (Apache Spark, ETLs development) – Preferred. Business Acumen: Strong ability to translate technical solutions into strategic business outcomes. You must be able to generate insights that go beyond what clients initially expect, providing additional layers of value and anticipating needs they haven't yet identified. #J-18808-Ljbffr