Aimpoint Digital

Platform Engineer

• Deliver engineering work on client-facing services and consulting projects, contributing directly to outcomes related data pipelines, platform deployments, observability, and infrastructure modernization specifically focused on Databricks
• Build internal tools, frameworks, and automations that reduce delivery friction and accelerate the onboarding, monitoring, and support of client environments
• Collaborate with Platform Services and Engineering team to develop and evolve delivery playbooks, onboarding guides, and Databricks platform best practices aligned with our initiatives
• Evaluate and implement new technologies that enhance platform resiliency, delivery velocity, and engineering consistency

Platform Services Engineer

Consultant

• Act as a trusted consultant, collaborating closely with clients across all levels and functions
• Complete both routine and ad-hoc analytical projects for external and internal stakeholders
• Lead live training and coach clients 1-on-1 on analytical and automation challenges
• Create knowledge base articles and formal training courses pertaining to analytical techniques and best practices

Analytics Consultant

• Lead end-to-end client engagements in Retail and CPG, from scoping and strategy to execution and delivery.
• Serve as a trusted advisor to clients, providing insights grounded in deep industry expertise.
• Collaborate with technical teams to design and implement AI, analytics, and operations research solutions tailored to Retail and CPG challenges.
• Translate complex data into actionable business recommendations for merchandising, supply chain, pricing, marketing, and operations.
• Mentor and guide team members on project execution and client management.
• Contribute to business development efforts, including proposals, thought leadership, demo and solution ideation and development.

Management Consultant – Retail & CPG

Management Consultant, Engagement Manager – Retail & CPG

Machine Learning Engineer

• Define high-level business objectives directly with clients
• Develop and execute the project plan to meet business objectives
• Proactively research data science space to deliver solutions
• Lead teams over the entire data science lifecycle
• Provide technical leadership and own specific technical components
• Manage client relationships and create value-driving initiatives
• Design and develop feature engineering pipelines
• Build ML & AI infrastructure and deploy models

Principal Machine Learning Engineer

• Work with stakeholders across all levels of an organization to determine how to create business value through analytics process automation, data visualization and statistical analyses
• Use expertise to build minimum viable products, production applications and/or enable organizations with the necessary skills through training and coaching
• Execute project work with guidance and minimal oversight
• Tools frequently used include Dataiku, Alteryx, Tableau, Power BI/Power Platform and Sigma Computing
• Depending on the needs of the client also use SQL and Python and work with cloud services such as AWS and Azure as well as modern database technologies such as Snowflake and Databricks

• Work independently to solve complex data science use-cases across various industries
• Become a trusted advisor working with clients to design end-to-end analytical solutions
• Design and develop feature engineering pipelines, build ML & AI infrastructure, deploy models, and orchestrate advanced analytical insights
• Write code in SQL, Python, and Spark following software engineering best practices
• Collaborate with stakeholders and customers to ensure successful project delivery

Lead Machine Learning Engineer

Solutions Engineer

• Lead end-to-end AI strategy engagements, including engagement planning, client management, delivery execution, and executive readouts.
• Lead AI and GenAI strategy workshops with business and technology stakeholders to identify, structure, and prioritize high-value use cases, with minimal oversight.
• Assess client current-state data, analytics, and AI maturity across people, process, technology, and governance; synthesize findings into clear strategic implications and recommendations.
• Define future-state AI capabilities, operating models, and governance frameworks that enable scalable, responsible, and value-driven AI adoption.
• Act as the AI solution architect within strategy engagements by designing conceptual and logical AI architectures, identifying appropriate AI patterns, and guiding platform and tooling decisions.
• Translate strategic intent into execution-ready artifacts, including AI roadmaps, use-case backlogs, reference architectures, and delivery sequencing plans.
• Partner closely across practice teams to ensure alignment between strategy, solution design, and downstream implementation.
• Evaluate technical options and tradeoffs and make informed recommendations grounded in business objectives.
• Develop business cases and value frameworks that quantify the expected impact of AI initiatives and support investment decisions.
• Lead executive-level communications, including steering committee updates, decision briefings, and final strategy readouts.

Platform Services Engineer

Analytics Consultant

Management Consultant – Retail & CPG

Management Consultant, Engagement Manager – Retail & CPG

Principal Machine Learning Engineer

Analytics Consultant

Lead Machine Learning Engineer

AI Solutions Architect