13 of 13 Statistical Inference Jobs

by business stakeholders, the model risk function, and other groups. Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and advanced applied experience. Experience in statistical inference and experimental design (such as probability, linear algebra, calculus). Data wrangling: understanding complex datasets, cleaning, reshaping, and joining messy datasets using Python. Practical expertise and work More ❯

full time in London 5 days a week. Preferred Experience with crypto and blockchain data. Strong data visualisation skills (esp. Graphana) Experience with events tracking data (esp. FullStory) Strong statistical or causal inference skills. Experience building data pipelines (esp. Dataform) Experience with Machine Learning methods. Additional Notes For Product Data Scientist Autonomous and Self-Motivated: here we want … clicked, what sessions they had, did they place an order, did they experience an error, etc.. Our tracking data comes from FullStory so experience with it is a plus. Statistical or Causal Inference: these capture non-experimental methods of data analysis. Essentially, can this person give us learnings and insights from non-experimental (observational) data. This work is … to model the impact of a change). Compensation & Package Significant upside Competitive salary Negotiable equity opportunities Skills: a/b testing,data analysis,events tracking,data pipelines,data,statistical inference,dataform,app,r,graphana,machine learning,causal inference,python,building,sql,crypto,data visualization,esp,it,plus,fullstory,projects,bigquery Seniority level Seniority level Associate Employment More ❯

years of professional working experience Someone who thrives in the incremental delivery of high quality production systems Proficiency in Java, Python, SQL, Jupyter Notebook Experience with Machine Learning and statistical inference. Understanding of ETL processes and data pipelines and ability to work closely with Machine Learning Engineers for product implementation Ability to communicate model objectives and performance to business More ❯

North America Business. As a Senior Data Modeller, you will develop and validate analytical solutions for diverse clients – this includes ad hoc data analysis as well as development of statistical models for predicting and managing the behavior of consumers. You will present results and business analysis to senior stakeholders (both internal and external) and support more junior team members … of its main analytical packages (such as Pandas, Numpy, scikit-learn) Understanding of how and when to apply analytical approaches including regression analysis and machine learning Grasp of probability, statistical inference, optimization algorithms, linear algebra, and calculus Good at spotting problems and quickly proposing solutions Experience with BI Tools such as Tableau and Power BI will be considered More ❯

to design and deploy machine learning services that are accessible via APIs for use in GUIs or direct access. Research, analyse and apply data sets using a variety of statistical and machine learning techniques. Support the analytical needs of the technical team inclusive of cleansing, mapping, statistical inferences, feature engineering and the bespoke data visualisation methods required by More ❯

to design and deploy machine learning services that are accessible via APIs for use in GUIs or direct access. Research, analyse and apply data sets using a variety of statistical and machine learning techniques. Support the analytical needs of the technical team inclusive of cleansing, mapping, statistical inferences, feature engineering and the bespoke data visualisation methods required by More ❯

Regulatory) Corporate credit risk models (IRB, PD/LGD/EAD) Competencies: Essential: Good background in Math and Probability theory - applied to finance. Good knowledge of Data Science and Statistical inference techniques. Good understanding of financial products. Good programming level in Python or R or equivalent. Good knowledge of simulation and numerical methods Awareness of latest technical developments More ❯

Regulatory) Corporate credit risk models (IRB, PD/LGD/EAD) Competencies: Essential: Good background in Math and Probability theory - applied to finance. Good knowledge of Data Science and Statistical inference techniques. Good understanding of financial products. Good programming level in Python or R or equivalent. Good knowledge of simulation and numerical methods Awareness of latest technical developments More ❯

Regulatory) Corporate credit risk models (IRB, PD/LGD/EAD) Competencies: Essential: Good background in Math and Probability theory - applied to finance. Good knowledge of Data Science and Statistical inference techniques. Good understanding of financial products. Good programming level in Python or R or equivalent. Good knowledge of simulation and numerical methods Awareness of latest technical developments More ❯

Regulatory) Corporate credit risk models (IRB, PD/LGD/EAD) Competencies: Essential: Good background in Math and Probability theory - applied to finance. Good knowledge of Data Science and Statistical inference techniques. Good understanding of financial products. Good programming level in Python or R or equivalent. Good knowledge of simulation and numerical methods Awareness of latest technical developments More ❯

SQL and Excel - Experience hiring and leading a high-performance team - Knowledge of data engineering pipelines, cloud solutions, ETL management, databases, visualizations and analytical platforms - Knowledge of methods for statistical inference (e.g. regression, experimental design, significance testing) PREFERRED QUALIFICATIONS - Knowledge of product experimentation (A/B testing) - Knowledge of a scripting language (Python, R, etc.) Our inclusive culture More ❯

style and format. Ability to effectively present information to top management, public groups, and/or boards of directors Ability to work with mathematical concepts such as probability and statistical inference, and fundamentals of plane and solid geometry and trigonometry. Ability to apply concepts such as fractions, percentages, ratios, and proportions to practical situations Ability to apply principles More ❯

style and format. Ability to effectively present information to top management, public groups, and/or boards of directors Ability to work with mathematical concepts such as probability and statistical inference, and fundamentals of plane and solid geometry and trigonometry. Ability to apply concepts such as fractions, percentages, ratios, and proportions to practical situations Ability to apply principles More ❯