The researcher will pursue a cross-disciplinary research project in optimization and adaptive experimental design for cross-scale scientific applications (lab-scale to pilot-scale) relevant to the DOE Office of Critical Minerals and Energy Innovation.
Compensation
Annual Salary Range: $76,600 - $126,400
Responsibilities
Develop optimization and sampling methods that leverage predictions from models trained on cross-scale heterogeneous evidence.
Develop sampling strategies to determine which experiments to run, measurements to collect, or operating conditions to use.
Develop original methodologies in numerical optimization, optimal experimental design, active learning, Bayesian optimization, sequential decision-making, and information theory.
Address adaptive sampling and surrogate-based optimization and statistical design of experiments.
Handle challenges arising from noisy, messy, missing, or sparse data.
The estimated salary range for this role, based in the United States of America is $130,000-$160,000. Compensation decisions are dependent on several factors including, but not limited to an individual's qualifications, location, internal equity, and alignment with market data. Additionally, employees are eligible to participate in one of our variable cash programs (bonus or commission) and eligible roles may receive equity as part of the compensation package.
Responsibilities
Build and orchestrate multi-agent AI systems using state-of-the-art platforms, enabling agents to collaborate on task planning, data retrieval, and execution across complex business workflows.
Implement and continuously refine prompt engineering strategies, including prompt chaining, evaluation, and guardrails to improve accuracy and reliability
Build and maintain RAG (Retrieval-Augmented Generation) pipelines, including Data ingestion and indexing, Embedding generation, Retrieval optimization & Response grounding and validation
Development of scalable backend services using Python frameworks for data processing
Design and manage embedding pipelines and vector search infrastructure using tools such as Pinecone, FAISS, Weaviate, Databricks or Snowflake
Requirements
Bachelor’s degree in computer science, Statistics, Data Science, Information Systems or related field
Demand Reporting Data Analyst to join Anglian Water's Strategic Asset Management team and help shape how consumption data is understood, reported and used across the business.
Compensation
Circa £34,231 (depending on Skills & Experience)
Responsibilities
Work proactively with stakeholders to understand their reporting and analysis needs, identifying where analytics can add value to consumption reporting and business decisions.
Keep stakeholders informed, agree clear expectations and manage the delivery of analytics products.
Source relevant data using your stakeholder network, data catalogues and data architecture models, supporting improvements to data quality where needed.
Cleanse, combine and analyse datasets using relevant analytical tools such as SQL, Power BI and Excel, applying an understanding of data modelling practices and reverse engineering existing models where required.
Create compelling reports, dashboards and visuals, applying user experience principles to make complex consumption data understandable and useful.
Build and develop models that will feed directly into regulatory reporting and consumption component of the water balance
Enrich stakeholder insight of our Big Data Smart Meter set, applying quality assurance techniques and automation processes to deliver consistent reporting
Develop statistical and predictive models that improve understanding of consumption, per capita consumption, business demand and leakage, and feed directly into regulatory reporting
Carry out exploratory analysis, data mining and empirical analysis to identify trends, drivers and opportunities
Apply data science techniques such as linear and multivariate regression, decision trees, Monte Carlo analysis and neural networks to complex business problems
Operations Analyst – Fixed Income Data & Analytics, Technology
Support operational processes for Fixed Income Data across Analytics, Risk, and Attribution functions, ensuring data quality and resolving issues.
Responsibilities
Support operational processes for Fixed Income Data across Analytics, Risk, and Attribution functions.
Review and resolve issues raised through fixed income analytics audits to ensure accuracy of security-level analytics and risk exposures within portfolio management systems.
Investigate and resolve data quality issues raised by users or identified through automated testing.
Follow operational best practices and ensure work is completed within daily SLAs.
Provide support for multiple third-party/vendor applications used in fixed income analytics and risk processes.
Requirements
Strong academic record in Finance, Mathematics, Physics, or a similar quantitative discipline
Design and build robust batch and real‑time data pipelines on GCP
Own data quality within delivery area, defining and implementing data contracts, quality checks, and observability instrumentation
Develop and maintain in‑house connectors and third‑party native integrations for IG’s ingestion estate
Contribute to engineering standards and best practices across the squad, including code review, dbt modelling patterns, CI/CD pipeline hygiene, and change control for shared datasets
Support feature engineering and model data pipelines for Data Science, helping lay foundations for AI‑ready data
Level 5 or higher qualification in Data Science, Computer Science, Statistics, Mathematics or a related quantitative subject, or equivalent professional experience