Role Description We are looking for a Staff Data Scientist to join the software team in the UK. - Building optimisation, forecasting and market participation algorithms to trade DER fleets in wholesale markets. - Modelling DER physical behaviour to connect control with market participation. - Operating large DER fleets in real time. - Quantifying uncertainty in asset behaviour and financial risk across strategies. Qualifications - 5+ years' experience as a Quantitative Developer, Data Scientist, Risk Analyst, or in a related field. - A good understanding of formal methods in quantitative finance. - A degree, MSc, or PhD in a quantitative field such as physics, mathematics, or statistics. - Professional experience with forecasting, optimisation, signal processing, control, and decision-making algorithms. - Professional experience with machine learning algorithms. - Extensive experience in one of Python, Julia, or OCaml, along with relevant libraries (e.g. pandas, TensorFlow, NumPyro). - Extensive experience with SQL and proficiency working with large datasets. - You love working in a smaller team where you can see the impact of your work. - You are a first-principles thinker with intellectual curiosity, a love of learning, and an openness to changing your mind when given new information. - You are autonomous and enjoy managing your own workload and deadlines. Requirements - Professional experience with Bayesian modelling. - Software architecture experience. - Experience with the functional programming paradigm. - Experience with TypeScript. - Professional experience in the energy, trading, or commodities sector. - Experience with Unix-like operating systems. Benefits - Competitive Compensation: Enjoy a competitive salary and stock options package. - Healthcare Benefits: Access to a private Medical Insurance scheme through BUPA (medical history disregarded). - Wellbeing Perks: A wellbeing and lifestyle benefits plan through Medicash. - Financial Security: 5% employer contributions pension matching. - Investment in Your Growth: A budget of £1,000 per financial year for work-related training and an allowance of 4 training days. - Eco-Friendly Commuting: Participate in the Cycle To Work scheme and benefit from the Workplace Nursery benefit. - Work Setup Support: Receive £200 upon joining for remote work setup. - Mental Health Support: Access to Spill - a Mental Health Support platform. - Family-Focused Benefits: Enhanced Maternity and Paternity Pay. - Time Off: 25 days of annual holiday entitlement plus 8 Bank Holidays. - Flexible Work Arrangements: Enjoy flexible start and finish times, with the option for fully remote working or a hybrid setup, depending on your preferences. Company Description Lunar Energy is committed to being an equal opportunities employer, welcoming applications from individuals regardless of their race, gender, ethnicity, disability, religion/belief, sexual orientation, gender identity or expression, nationality, age, or social background. We actively encourage applications from underrepresented and minority groups, and we do not filter applications by university background, welcoming those who have taken alternative educational and career paths.

• Work closely with client's Quantitative Sciences (QS) staff to support program needs in a timely manner. • Perform end-to-end data analyses, from hypotheses formulation, experimental design, writing analysis plans, data cleaning, executing analysis, and preparing reports and documentation. • Strengthen client’s advanced analytics toolkit by identifying and applying emerging techniques, as well as by advancing the state of the art and developing novel analysis tools as needed. • Collaboration with QS staff and their cross-functional collaborators to understand disease biology and to identify or develop appropriate goals for data science work (for example identifying sensitive and specific endpoints for segmenting patient population, tracking disease progression, quantifying quality of life, etc.) • Work closely with client's statisticians to ensure statistical issues in data analysis are addressed as needed. • Anticipate and communicate internal and external resource and quality issues that may impact deliverables or timelines of the program. Propose and implement solutions. Escalate issues to management as appropriate in a timely manner.

Role Description Buscamos un/a Machine Learning Engineer / AI Data Scientist. ¿Te apasiona convertir datos en soluciones inteligentes? Buscamos un perfil con sólida base en Machine Learning e Inteligencia Artificial para participar en el desarrollo, validación e industrialización de modelos aplicados a casos de negocio de alto impacto dentro del sector de banca de inversión. Trabajarás junto a científicos de datos, ingenieros y equipos de negocio desarrollando soluciones escalables que abarcan todo el ciclo de vida del modelo: desde la exploración de datos hasta su despliegue y monitorización en producción. Principales responsabilidades: - Diseñar, desarrollar y validar modelos de Machine Learning e Inteligencia Artificial para casos de uso de banca corporativa y de inversión. - Aplicar técnicas avanzadas de aprendizaje supervisado y no supervisado, forecasting, NLP, detección de anomalías y modelos predictivos. - Realizar análisis exploratorio de datos, ingeniería de variables y evaluación de modelos utilizando metodologías estadísticas rigurosas. - Construir pipelines reproducibles de entrenamiento, inferencia y monitorización siguiendo buenas prácticas de ingeniería. - Implementar procesos de validación y gobernanza de modelos, incluyendo explicabilidad, robustez y documentación. - Colaborar con equipos técnicos y de negocio para transformar necesidades funcionales en soluciones analíticas de alto valor. - Investigar y aplicar nuevas tecnologías relacionadas con Deep Learning, LLMs e Inteligencia Artificial Generativa. Qualifications - Titulación en Informática, Matemáticas, Estadística, Data Science, Machine Learning, Ingeniería, Física o disciplinas afines. - Sólidos conocimientos de álgebra lineal, probabilidad, estadística, optimización y métodos numéricos. - Experiencia desarrollando en Python aplicando buenas prácticas de programación. - Experiencia con librerías de Machine Learning como scikit-learn. - Conocimiento de al menos un framework de Deep Learning (PyTorch o TensorFlow). - Experiencia en evaluación y validación de modelos (AUC, Precision/Recall, RMSE, calibración, etc.). - Manejo de pandas, NumPy y consultas SQL. - Capacidad para trabajar en equipos multidisciplinares y comunicar resultados técnicos y de negocio. - Nivel profesional de inglés C1. Requirements - Deseables: - Experiencia previa como Machine Learning Engineer o Data Scientist. - Conocimientos de NLP, Transformers, Embeddings, LLMs e IA Generativa. - Experiencia con conceptos y herramientas MLOps (MLflow, Docker, CI/CD, monitorización de modelos). - Experiencia en plataformas cloud (AWS, Azure o GCP). - Conocimiento de Spark y procesamiento distribuido. - Experiencia o interés en Databricks. - Conocimientos en series temporales, inferencia causal o stress testing. - Experiencia en entornos de banca corporativa o de inversión. - Conocimiento de gobierno y riesgo de modelos en entornos regulados. - Familiaridad con métricas financieras, gestión de riesgos o analítica bancaria. Benefits - Contrato indefinido con salario competitivo. - Modalidad 100% remota. - Plan de carrera personalizado y formación continua. - Participación en proyectos estratégicos de IA y Machine Learning con impacto directo en el negocio. - Trabajo con tecnologías punteras en Inteligencia Artificial, Deep Learning y LLMs. - Colaboración con equipos internacionales altamente especializados. - Flexibilidad horaria y equilibrio entre vida personal y profesional. - Beneficios sociales adaptados a tus necesidades.

• Work with partner ML and Annotation engineers and TPMs to spec out data and training requirements • Ensure that datasets are correctly curated, filtered, and processed to meet the rigorous standards of production-level machine learning • Act as a technical translator, taking high-level project goals and turning them into actionable data specs for engineering and annotation teams • Maintain high standards for data integrity, ensuring that the media datasets used for training are representative and accurate