Euristiq

Join us in developing a data science and ML offering for a comprehensive workforce management platform serving enterprises across industries and regions. We are looking for a Senior Data Scientist to own the direction and delivery of the Intelligence Pack. This analytical and ML layer turns platform data into predictive value for customers.

This is the first dedicated data science hire on the project. You will set standards, shape the roadmap in partnership with the Product team, and deliver much of the work yourself – across the full analytics lifecycle: from data architecture and predictive insights to customer-facing dashboards. The role requires a strong combination of technical depth, product mindset, and the ability to work autonomously across multiple stakeholders.
Enterprise SaaS platform for workforce management and talent acquisition.

Requirements

• Strong applied data science skills: building predictive models, surfacing patterns, and shipping ML/AI features into production
• Excellent SQL and data modelling skills; ability to design, refactor, and optimise warehouse structures
• Proven experience delivering customer-facing dashboards (Metabase or similar) where accuracy and performance both matter
• Hands-on experience with query optimisation, version control, and structured analytics development practices
• Product-minded approach: ability to think about users, value, and trade-offs as naturally as about schemas
• Experience helping define the product direction or roadmap for a data product, intelligence layer, or analytics offering
• Credibility to set standards and lead through influence as a senior individual contributor
• Strong communicator across product, engineering, commercial, operations, and customer audiences
• Comfortable engaging directly with customers on reporting and insights
• Upper-Intermediate English level or higher

Would Be a Plus

• Experience introducing tooling and processes that speed up analytics development while improving quality and consistency
• Experience working across analytics engineering, business intelligence, and data science disciplines within a single role
• Experience supporting customer-facing analytics, reporting, or intelligence products
• Interest in growing a data capability and taking on broader leadership responsibilities as the function scales

Responsibilities

• Own the long-term direction of the Intelligence offering, spanning data models, analytics, and customer-facing insights
• Partner with the Product team to build and evolve the data science roadmap
• Design and evolve the data architecture and canonical data model underpinning Intelligence
• Develop and ship data science work: predictive signals, pattern detection, and automated insights
• Design and deliver dashboards that customers trust and rely on
• Own analytics engineering end-to-end: modelling data, optimising queries, versioning work, and reviewing changes before they reach customers
• Establish architectural principles, analytical standards, and ways of working that allow Intelligence to scale
• Work closely with Engineering to ship Intelligence features into the product
• Engage directly with customers to understand reporting needs, explain insights, and validate value
• Support Commercial and Operations teams with insights that underpin upsell, renewal, and decision-making
• Provide insights and what-if analysis tooling for C-Suite customers

Join us in building advanced forecasting and optimization capabilities for an energy trading company participating in electricity markets across multiple regions. We are looking for a Data Scientist to own the development of predictive models that directly support our trading team's market decisions. 

This is the first dedicated data science hire on the project. You will establish modeling standards, define best practices, and deliver the work yourself — across the full data science lifecycle: from time series forecasting and price prediction to battery optimization and model governance. The role requires strong technical depth and the ability to engage directly with business stakeholders in a domain where the problems are genuinely hard.

Key Responsibilities

• Design, build, and validate time-series forecasting models for facility load, market demand, and electricity price signals
• Select and tune appropriate modeling approaches (statistical, ML, and deep learning) based on the forecasting horizon, data availability, and accuracy requirements
• Develop and maintain ML pipelines on the Databricks platform using PySpark, MLflow, and Delta Lake
• Partner with domain experts to incorporate market structures, tariff rules, and grid dynamics into model features and evaluation frameworks
• Communicate model performance, assumptions, and limitations to both technical and non-technical stakeholders

Requirements

1. Time-Series Forecasting Expertise
 

You have hands-on experience with a broad range of forecasting methods and know when to reach for each one. This includes classical statistical approaches, gradient boosting methods, and modern deep learning architectures. You understand the practical challenges of time-series work: feature engineering with lags and rolling windows, handling seasonality and regime changes, proper train/validation/test splitting, and avoiding data leakage.

2. Databricks Platform Proficiency
 

You are comfortable working end-to-end in Databricks — from exploratory analysis in notebooks to productionizing models via MLflow tracking, model registry, and scheduled jobs or workflows. Experience with Delta Lake for versioned, audit-friendly data pipelines is a strong asset. Familiarity with Unity Catalog and Databricks Feature Store is a plus.

Nice to Have

• You have worked in or alongside the electricity sector and understand the data and dynamics that drive it
• Experience with graph-based machine learning or graph neural networks (GNNs)
• Experience with probabilistic forecasting (prediction intervals, quantile regression, conformal prediction)
• Exposure to real-time or near-real-time inference pipelines
• Familiarity with weather data integration for load and renewable forecasting
• Familiarity with battery storage optimization or operations research methods for energy asset dispatch