Data Scientist — Energy / NILM / Time-Series

About Flock Energy Flock Energy is a London, UK, HQed smart energy startup, building an

energy data-as-a-service platform designed to become the digital infrastructure underpinning the clean energy transition. Our systems transform raw meter and contextual data into actionable insights using machine learning and AI, while generating high-quality datasets through real-world energy systems and device integrations. Flock offers a suite of solutions to utilities, consumers, and energy ecosystem participants (OEMs, developers, etc.). We work across markets — with

live projects in Singapore, the United Kingdom, and India — partnering with utilities, consumers, OEMs and energy service providers to optimize their operations through custom built models. Our team blends data science, embedded engineering, and energy domain expertise to ship products that meaningfully change how energy is consumed, attributed, and saved.

Role Overview We are looking for a Data Scientist to help further refine the core intelligence layer of our platform, with a primary focus on load disaggregation (NILM), baseline modeling, and energy savings validation. This is a high-ownership role critical to both validating system performance and enabling the broader data platform. You will work directly with raw meter, smart-devices, and environmental sensor data — turning noisy, real-world signals into the models that our Singapore, UK, and India deployments rely on.

Key Responsibilities

• Develop models for non-intrusive load monitoring (NILM) to identify appliance-level usage from aggregate energy data.
• Build and refine baseline consumption models for measurement & verification (M&V) of energy savings.
• Analyze time-series data from smart meters, smart plugs, and environmental sensors to surface reliable patterns and anomalies.
• Integrate electrical and power-related parameters (voltage, current, power factor, harmonics) into modeling efforts.
• Improve attribution accuracy of energy savings and overall system performance across deployments.
• Work closely with hardware and software teams to align data pipelines, model assumptions, and observed system behavior.

Requirements

• 3–6 years of experience in data science, applied ML, or a closely related field.
• Strong experience with time-series analysis and signal processing.
• Proficiency in Python and the standard data science / ML stack (NumPy, pandas, scikit-learn, PyTorch or TensorFlow).
• Strong analytical thinking and structured problem-solving skills.

Preferred Background

• Direct experience with NILM, load disaggregation, or energy analytics.
• Background in electrical engineering, physics, or a related quantitative domain.
• Familiarity with power systems, smart-meter data, or building energy management.

What We’re Looking For

• Ability to work with noisy, real-world data and still derive reliable insights.
• Strong ownership in building, validating, and maintaining core system models in production.
• Genuine interest in energy systems, physical processes, and the climate transition.

What We Offer

• Competitive salary and meaningful equity participation.
• Exposure to international teams and problem statements across our Singapore, UK, and India projects.
• Real-world impact — improving energy access, efficiency, and reliability for end users.
• Flexible working hours and a high-trust, ownership-first culture.
• A team of passionate operators, engineers, and scientists building in one of the most consequential sectors of the next decade.
• Access to large, real-world energy datasets and the tooling to do serious work with them.

How to Apply Send your CV (and a short note on why this role interests you) to

careers@flockenergy.co.uk . Please include the role title in the subject line. We read every application and aim to respond within 2 weeks. Flock Energy is an equal opportunity employer. We hire on merit and build teams that reflect the markets we serve.