Sr. Data Scientist

We are looking for a senior data scientist / reports lead to deliver statistically sound, production-ready, decision-relevant models and analyses that measurably improve product outcomes and operational performance, and/or to lead the reporting function that delivers trusted, timely reporting to the business. The role is senior and hands-on: framing problems, building and validating models, and translating results into actionable insight and board-level decisions.

The defining shift for this seat in 2026 is that the field has bifurcated — organizations are cutting generic "data scientist" headcount while aggressively hiring specialists who ship production systems, evaluate LLM outputs rigorously, or translate models into decisions. Even modeling roles now absorb MLOps expectations, and model drift is the leading cause of production ML failure. For GenAI, validating a system you cannot fully inspect shifts the craft from replication to outcome-based test design, robustness testing, and monitoring for silent drift.

The value this person brings is rigorous, decision-driving data science and trustworthy reporting, accelerated by governed AI and protected by disciplined validation.

We are seeking a rigorous, evidence-driven data professional who is honest about uncertainty, communicates complex results clearly, and can catch confident errors before they reach a user. They have a governance and validation mindset and collaborate well with business and engineering.

This is a senior profile that partners across analysts, data engineering, MLOps, business, and BI. It suits someone open to a hybrid or remote-first engagement, and to mobilization to the Middle East, Europe, or North America depending on the client. The person we want values rigor over hype and ships things that hold up in production.

A strong candidate for this role has built and validated models that drove decisions and/or led trusted reporting — someone who has maintained statistical rigor, productionized models, and validated AI-produced outputs. They bring an openness to using generative / agentic AI with governance.

In practice, the responsibilities break down as follows:

• Data science: Frame problems; build, validate, and productionize models; translate results into actionable insight and decisions.
• Reporting leadership: Deliver accurate, timely, governed reporting; lead reporting standards and automation.
• Rigor, validation & MLOps: Ensure statistical rigor; apply risk-tiered, outcome-based evaluation, robustness testing, and drift monitoring for models and GenAI outputs; manage the data-train-deploy-monitor-retrain loop.
• AI-accelerated work: Use generative / agentic AI (Amazon Bedrock or equivalent) for analysis, feature generation, and automated reporting, with governance and validation.

We want to see a data scientist with genuine statistical and ML depth who validates rigorously and communicates clearly, and who uses generative AI carefully — validated, monitored, and governed.

Core data science

• Statistics / ML, Python / R, SQL; model validation, deployment, and monitoring.
• Data visualization and reporting; feature engineering.
• MLOps fundamentals; cloud ML awareness (AWS, Azure, GCP or equivalent).

Data science performance (KPIs from research)

• Steers model performance/validation, insight adoption, reporting accuracy/timeliness, and drift/robustness — with drift monitoring central to production reliability.

Modern Agentic AI / AIOps (niche areas we are seeking — depth explored at interview)

• Rigorous LLM/GenAI evaluation: outcome-based tests, robustness testing, and monitoring for silent drift when you cannot inspect the model.
• Agents that assist analysis and reporting; governed foundation-model use (Amazon Bedrock or equivalent) for feature generation.
• Model / data-drift and pipeline monitoring within an MLOps/LLMOps loop.

This role is delivered through Solutions Chapter Group's Human Resources chapter (Chapter 12) as a Recruitment Process Outsourcing (RPO) engagement, under our Technical Recruitment stream. Rather than a "post-and-pray" marketplace approach, we use a Curated Professional Recruitment model: candidate profiles are pre-vetted in our internal database, and when a required profile isn't already available, our dedicated Scout network conducts global headhunting to secure it. We screen and match candidates against each client's specific requirements, and placements may be remote or an on-site deployment with mobilization to the Middle East, Europe, or the United States.

From a recruitment-fit perspective, we screen for:

• Rigorous, evidence-driven work; honest about uncertainty.
• The ability to translate complex results clearly.
• A governance, validation, and drift-monitoring mindset.
• Collaboration with business and engineering.
• Openness to the engagement model (remote or mobilized on-site) and readiness for cross-region deployment.

Areas to explore at interview *(topics only)*

• A model you built and productionized
• Trustworthy reporting