Data Scientist

We are looking for a Senior Data Scientist to build the intelligence layer that sits at the heart of our platform. This is not a generic analytics role. You will design and own the models, measurement frameworks, and signal pipelines that convert raw workforce telemetry, time-use, application activity, workflow events, and AI tool adoption into the RoI signals that enterprise leaders use to make decisions.

You will work at the intersection of data science, applied ML, and product thinking. The intelligence you build will directly power the platform Agent, the benchmarking engine, AI ROI measurement, and anomaly detection across human and digital worker activity. This is a high-ownership role with direct impact on how the platform is positioned as an AI-first productivity governance platform.

The candidate will have responsibilities across the following functions:

Intelligence Layer and Signal Engineering:

• Design and own the data science foundation of the platform intelligence layer: the models and pipelines that transform telemetry streams into productivity signals, anomalies, benchmarks, and eGDP indicators.
• Build time-series models to detect workload patterns, productivity variance, idle capacity, context switching, and workflow fragmentation across teams, locations, and business functions.
• Develop anomaly and outlier detection systems that surface hidden inefficiencies, rework, wait-states, underutilisation, and SLA leakage without generating noise that erodes trust.
• Design the statistical framework for fair benchmarking and peer-group comparisons across diverse teams, process types, and geographies.

Human + AI Productivity Measurement:

• Build the measurement models that quantify the impact of AI tool adoption, copilots, automation, and agents on actual work output, cycle time, and effort distribution
• Design before/after and counterfactual frameworks to give enterprise buyers defensible AI ROI evidence, not just adoption dashboards.
• Create the data layer that allows the platform to track human work, AI-assisted work, and automated work as a unified productivity system, the foundation of eGDP.
• Work closely with product and engineering to ensure that measurement outputs are interpretable, auditable, and trustworthy for CFO- and COO-level review.

ML Model Development and Productionisation:

• Develop, evaluate, and ship production-grade ML models for classification, regression, clustering, and time-series using Python-based ML stacks.
• Own model performance over time: monitoring for drift, retraining cadence, and quality gates that ensure intelligence remains accurate as customer data scales. Partner with engineering to embed models into real-time and batch pipelines with appropriate latency, reliability, and privacy controls.
• Document modelling decisions, assumptions, and limitations rigorously so that outputs can be explained to non-technical stakeholders.

Collaboration and Product Partnership:

• Work directly with the AI product team to translate intelligence capabilities into product features, translating model outputs into insights, recommendations, and actionable workflows.
• Partner with the AI Systems Engineer to ensure that the intelligence layer provides clean, well-structured context for LLM reasoning and agent orchestration
• Contribute to the roadmap for our benchmarking engine, anomaly alerts, and AI ROI reporting features.
• Engage with customer success and solutions teams to understand how intelligence outputs are used in enterprise decision-making and continuously improve relevance.

Requirements:

• Experience: 3-5 years in applied data science or ML engineering, with a track record of shipping production models in a B2B product or enterprise software environment.
• Time-series expertise: Strong hands-on experience with time-series analysis, behavioural signal processing, or sequential data modelling, not just tabular ML.
• Statistical rigour: Deep working knowledge of statistical inference, causal reasoning, experimental design, and benchmarking methodology.
• ML engineering: Proficiency in Python (pandas, NumPy, scikit-learn, PyTorch or equivalent); experience with model productionisation, not just notebook-based analysis.
• Anomaly and pattern detection: Demonstrable experience building anomaly detection, clustering, or behavioural pattern models in complex, noisy real-world data.
• Communication: Ability to explain model outputs and uncertainty clearly to product, engineering, and business audiences without over-simplifying or hiding limitations.

Preferred Qualifications:

• Experience with workforce analytics, operational telemetry, clickstream data, or digital activity data.
• Familiarity with LLM-adjacent data work: embedding generation, semantic clustering, retrieval-relevant feature engineering.
• Experience with AI ROI measurement, A/B testing at scale, or causal inference in production environments.
• Knowledge of privacy-preserving analytics techniques relevant to enterprise workforce data (aggregation, differential privacy concepts, data minimisation).
• Cloud data platform experience AWS, Azure, or GCP and familiarity with large-scale pipeline orchestration (Airflow, dbt, Spark).