10 of 10 Statistical Inference Jobs

shaping the future of marketing technology — and actually enjoys doing it — you're in the right place. Find us on Instagram: @optimizely Introduction Rigorous statistical analysis is a core to all experimentation products at Optimizely. You will get on customer calls to articulate how we maintain statistical accuracy … products as well as triage questions around statistical inference. You will educate internal and external stakeholders about fundamental as well as cutting edge statistics concepts via webinars, speaking engagements, blog posts, and white papers. You will also have the opportunity to work with our product and engineering teams ...

Early-Career Award and will be based in a new group within the Department of Infectious Disease Epidemiology. The group develops scalable mathematical and statistical approaches leveraging large pathogen genome datasets to understand the determinants of infectious disease transmission . Our work combines data analysis, genomic epidemiology, mathematical modelling … statistical inference to answer public health relevant questions. We are committed to building a positive and inclusive environment, where all team members feel that they belong and are excited to do research. We recognise the systemic barriers that exist in higher education and research, particularly for people from ...

Highly proficient in writing optimized, complex queries across large cloud data warehouses (e.g., Snowflake, BigQuery). Programming & Modelling: Strong proficiency in Python for statistical modeling, forecasting, and machine learning. Statistics & Math: Deep understanding of statistical inference, regression analysis, probability theory, decision trees, and hypothesis testing (p-values … quantitative analytics or data science role in a fast‐paced environment. Business Acumen: Demonstrated track record of framing business problems into mathematical/statistical frameworks and outputting actionable recommendations. Communication: Ability to distill complex statistical concepts into clear, plain language for non‐technical executives. Nice‐to‐Haves: Experience ...

client’s team in Central London. You’ll play a pivotal role in driving data-informed decision-making through advanced A/B testing, statistical analysis, and enterprise-level business intelligence solutions This is a client-facing position where you’ll work embedded within the client’s team, requiring … initiative, and the ability to represent CI&T professionally Design, implement, and analyze A/B tests to measure product and feature performance Apply statistical methods to ensure test validity and generate actionable insights Present findings and recommendations to stakeholders Design, build, and optimize semantic data models in Power ...

join our Research and Intelligence organisation in London. You'll work across flexible, cross-functional squads within the Data Science team, leading analytical, statistical, and machine-learning work across both UTXO and EVM blockchains. The team develops behavioural heuristics, graph algorithms, statistical models, and machine-learning techniques that … projects or systems, translating broad problem statements into actionable work, managing evolving requirements, and delivering measurable outcomes. Design, develop, and validate novel analytical methods, statistical models, behavioural heuristics, and algorithms across UTXO, EVM, and other blockchain data to attribute on-chain activity and uncover customer-relevant insights. Stay current ...

systems end to end – from hypothesis through experimentation, training, evaluation, deployment, monitoring and iteration. You’ll write production-quality training and inference code, own your releases, and be accountable for the business metrics your models improve. You Will Own ML systems end to end – hypothesis, experimentation, training, evaluation, deployment … monitoring and iteration. Write production-quality training and inference code, own releases, and be accountable for the business metrics your models improve. Design, execute and interpret A/B and multivariate experiments – power analysis, guardrails, exposure/assignment strategy and statistical significance. Apply contextual bandits, causal inference ...

stage of the consumer’s lifecycle. Experience overseeing experimentation programmes and practices that inform product or business decisions. A good understanding of methods for statistical inference (e.g. Bayesian, Frequentist) and familiarity with predictive analytics techniques is highly beneficial. Working knowledge of common analytics tools used for tracking ...

heuristics and algorithms over EVM (and other) blockchain data to attribute on‐chain activity and uncover new insights. Apply advanced techniques including graph computation, statistical modeling, and machine learning to large‐scale on‐chain datasets. Identify and resolve inefficiencies in code, methodology, and workflows; ensure your work integrates cleanly … Python and SQL, with a track record of writing clean, testable, production‐quality code. Demonstrated experience applying advanced analytical techniques (e.g. graph algorithms, ML, statistical inference) to large, messy datasets. Comfort working with EVM‐based blockchain data, or strong evidence of the aptitude to ramp up quickly ...

Engineering. 3–5 years as a quantitative analyst/programmer in asset management or investment banking. Strong grounding in probability theory, stochastic calculus and statistical inference. Experience across liquid and illiquid assets, asset allocation and portfolio optimisation. Practical exposure to bond pricing, stochastic volatility modelling and Monte Carlo simulations. ...

opportunity for a Machine Learning Engineer with a strong quantitative background to work on real‐world prediction problems within financial markets. The role combines statistical modelling, machine learning research, natural language processing, and large language model applications in a production investment environment. You will develop predictive models across fixed … issuer credit deterioration, transaction costs, liquidity forecasting, and relative‐value opportunities. Apply machine learning techniques to low signal‐to‐noise datasets where robustness and statistical discipline are critical. Conduct extensive out‐of‐sample testing and validation to ensure model reliability and performance. Evaluate model effectiveness using appropriate statistical ...