Senior Associate - Lead Data Scientist

Job Purpose

A Lead role at Acuity blends technical leadership with consultancy excellence. Leads act as trusted advisors, forging strong client relationships and communicating complex concepts clearly to both technical and business audiences. They own the delivery of complex projects end-to-end, proactively solving challenges and ensuring solutions deliver measurable business impact. Leads shape and oversee architectural decisions, mentor peers, and drive internal capability building, consistently linking outputs—such as ML predictions and automation—to financial outcomes and strategic case studies.

With an MLOps focus, leads design and maintain robust, scalable ML pipelines, ensuring reproducibility and compliance across solutions. They automate deployment, monitor performance, manage CI/CD workflows, and optimise infrastructure for efficiency and cost-effectiveness. Leads enforce best practices in versioning, testing, and governance, champion reliability and security, and stay ahead of emerging technologies and business trends—driving adoption of modern tools (e.g., Azure ML, Databricks) and fostering operational excellence.

Desired Skills And Experience

As a Lead MLOps Engineer, you will

• Core Engineering & Languages: Expert in Python and MLflow; R experience desirable. Strong software engineering practices (testing, packaging, CI/CD).
• Architecture & MLOps: Proven design of scalable ML architectures and end‑to‑end MLOps systems (training, evaluation, lifecycle management, monitoring/alerting, feature stores, experiment tracking).
• Distributed & Performance: Proficiency with distributed computing for large‑scale data processing, model training, and high‑throughput serving.
• Cloud & Platforms: Deep hands‑on with Azure and/or other platform services (Entra, Storage, Monitoring, ACR, Databricks) and containerization (Docker); comfortable with WSL and Python wheel packaging.
• Governance, Security & Compliance: Enforce versioning, testing, observability, model governance, and secure deployment across environments.
• GenAI/LLM: Familiarity with GenAI solution patterns—RAG architectures, embeddings, prompt engineering, and cloud deployment of LLMs.
• Accelerators & Standardization: Experience using/defining MLOps accelerators to standardize project setup, deployment, and monitoring.
• Leadership: Ability to set technical direction, make pragmatic trade‑offs between technical debt and business value, and communicate decisions to stakeholders
  
  

• Core Engineering & Languages: Expert in Python and MLflow; R experience desirable. Strong software engineering practices (testing, packaging, CI/CD).
• Architecture & MLOps: Proven design of scalable ML architectures and end‑to‑end MLOps systems (training, evaluation, lifecycle management, monitoring/alerting, feature stores, experiment tracking).
• Distributed & Performance: Proficiency with distributed computing for large‑scale data processing, model training, and high‑throughput serving.
• Cloud & Platforms: Deep hands‑on with Azure and/or other platform services (Entra, Storage, Monitoring, ACR, Databricks) and containerization (Docker); comfortable with WSL and Python wheel packaging.
• Governance, Security & Compliance: Enforce versioning, testing, observability, model governance, and secure deployment across environments.
• GenAI/LLM: Familiarity with GenAI solution patterns—RAG architectures, embeddings, prompt engineering, and cloud deployment of LLMs.
• Accelerators & Standardization: Experience using/defining MLOps accelerators to standardize project setup, deployment, and monitoring.
• Leadership: Ability to set technical direction, make pragmatic trade‑offs between technical debt and business value, and communicate decisions to stakeholders