Duties: Participate in the development process of ML based financial products as a technical leader; Collaborate with colleagues on production machine-learning systems and applications; Design, experiment, and evaluate software systems; Write, test, and maintain production-quality code; Represent Bloomberg at conferences and open-source communities; Demonstrate technical leadership by owning cross-team projects; Build the libraries and frameworks that support fault tolerant and testable systems
Requirements: A Degree in Computer Science, Engineering, Mathematics, similar field of study or equivalent work experience; 5+ years of experience building and deploying Machine Learning models to production; 2+ years of experience in leadership, managing and coaching globally distributed teams and influencing third party technology strategy; Extensive experience in software engineering

Duties: We are searching for an outstanding Bioinformatics Engineer with an interest in working closely with computational biologists and experimental biologists to deploy bioinformatic methods and resources relevant to single-cell sequencing and spatial imaging. The focus of this work is the building and maintenance of software tools, pipelines, and resources for integrative analysis of high throughput sequencing and imaging data
Requirements: A PhD degree or equivalent experience in biological sciences, mathematics, physics, bioinformatics, or related discipline with a quantitative background; Knowledge of sequencing and imaging technologies and their application; Proven experience with computational tools and workflows for the analysis of genomic datasets; Strong programming skills and proficiency in scientific computing software and scripting languages such as Python and R

Duties: Conduct research on single-molecule biophysics; Mentoring graduate students in research projects
Requirements: The candidate should have a Ph.D. in physics, biophysics, chemical biology, bioengineering, or a related field

Duties: The Department of Mathematics at the University of Rochester invites applications for several non-tenure track Visiting Assistant Professor positions for new or recent PhDs. The positions are for two years, renewable for a third upon satisfactory performance, and carry a 2-2 annual teaching load. Areas represented by the department, including (1) algebraic topology, (2) analysis, (3) combinatorics, (4) differential geometry and global analysis, (5) number theory, and (6) probability, will be preferred
Requirements: Qualifications include a PhD in mathematics, strong evidence of excellence in the teaching of undergraduates, and outstanding promise in research

Duties: Support global ERM initiatives such as Own Risk and Solvency Assessment (ORSA), internal economic capital modeling, risk appetite & limit framework, catastrophe modeling, and rating agency capital modeling such as AM Best BCAR; Support regulatory solvency and stress test filing in various jurisdictions, in both US and Non-US; Assist capital allocation and Return on Equity attribution by profit center, for both underwriting and investment operations; Provide quarterly reserving analysis using appropriate reserve methodologies and procedures to ensure timely and reasonable recommendations on loss reserves by line and legal entity; Improve reserving data management and reserving tools to enhance efficiency of the reserve review and booking process
Requirements: Bachelor’s degree in Math, Statistics, Finance, Actuarial Science, Accounting, Economics, or other quantitative field. ACAS-FCAS or close to ACAS. 7-12 years of experience in a relevant function (actuarial, enterprise risk management, finance, reinsurance) of the P&C insurance industry. Some P&C loss reserving experience is required. Proficiency in MS Excel is required; programming skills in SQL, VBA, Python, R and other languages is desired. Effective communication and ability to collaborate with different functions across the organization. Knowledge of insurance financials (GAAP, Statutory, IFRS 17) as well as insurance and reinsurance operations is a plus. Knowledge of NAIC ORSA, Solvency II and other global insurance regulatory frameworks is a plus

Duties: Oversee teams of Engagement Leads and Deployment Leads who execute day to day customer operations. This includes ensuring customer data is ready for our platform, training new users, identifying insights, escalating and troubleshooting issues, creating value stories, and working with customers; Ensure platform deployments run on schedule and customers understand their data and relevant problems to be solved with the platform; Work closely with senior executive leadership at customer companies to share insights and value stories; Ensure customers are receiving support and guidance on technical issues and that the team is working closely with customer stakeholders to understand their technical requirements and objectives
Requirements: 7+ years as a client/customer-facing project lead, managing teams and working closely with high level executives; 3+ years as a people manager; Leverages problem solving, creativity and technical skills to help organizations use their data to drive impact in the world

Duties: Conduct hands on analytics and perform model development procedures independently to support the model development and analytics efforts across customer lifecycle (Acquisitions, Customer Management, Collections). Develop model in compliance with Global Risk and external regulatory guidelines. Explore modeling techniques including new machine learning methods and their application in solving business problems across all account management strategies
Requirements: Bachelor’s degree, or foreign equivalent, in Statistics, Mathematics, Applied Analytics, Economics or a related field, and six (6) years of experience

Duties: Responsible for the delivery of end results focused on operational excellence; Makes decisions guided by function/business plans and policies that impacts the operations of a sub-function or work area within a sub-function
Requirements: Recognized as an expert within the company; Anticipates internal and external business challenges and/or regulatory issues; recommends process, product or service improvements; Solves unique and complex problems; requires conceptual and innovative thinking to develop solutions; Adapts function/business strategy plans and policies to address service and/or operational challenges; May play key role in projects, processes or vendor management

Duties: The Department of Mathematics and Statistics at Skidmore College invites applications for a full-time Visiting Assistant Professor or Visiting Lecturer for the 2024-25 academic year. This is a one-year position. The position involves teaching 18 contact hours per year, which is typically a 3: 2 course load
Requirements: A doctorate (or ABD status) in statistics, biostatistics, or a closely related field is required for the Visiting Assistant Professor position. A master’s degree in statistics, biostatistics, or a closely related field is required for the Visiting Lecturer position. Preference will be given to candidates with teaching experience. The successful candidate will be able to teach entry-level courses in our statistics curriculum, including our introductory statistics and statistical methods courses

Duties: Develop analytical libraries used for pricing, market analysis and risk-management for Fixed Income Market business. Develop optimization tools for calculating optimal balance sheet, liquidity, and capital charges. Develop web-based front-end interface for stakeholders to view and interact with analytical tools. Create, implement, and support quantitative models for the Fixed Income trading business leveraging a wide variety of mathematical and computer science methods and tools including, mathematical finance, statistics and probability and software engineering. Apply advanced coding skills to code and debug code
Requirements: Requires a Master’s degree, or foreign equivalent in Financial Engineering, Financial Mathematics, Operations Research and Information Engineering, or related quantitative field and 2 years of experience as a Quantitative Analyst, or related position involving quantitative statistical modeling and data analysis for a global financial services institution

Duties: As part of the company's Center for Data Science and Artificial Intelligence (CDSAi) corporate analytics group, applies analytical skills to work on all aspects of the life insurance value chain, ranging from risk models, fraud detection, customer behavior study, process triaging, and marketing prediction to a variety of other analytics solutions. Applies technical data, analytical, and programming skills to ingest, wrangle, and explore external and internal data to create data assets and reports
Requirements: Master's degree in Statistics, Analytics, Computer Science, Mathematics or Machine-Learning (willing to accept foreign education equivalent) and three (3) years of experience performing data analytics and statistical modeling using complex large-sized datasets in the consumer finance domain

Duties: Develop and optimize scalable ML pipelines to handle large datasets, train intricate models, and seamlessly deploy them into production; Collaborate closely with diverse teams across behavior, perception, research, mapping, and infrastructure to design and implement comprehensive ML solutions; Create frameworks and interfaces to accelerate machine learning development and maximize hardware efficiency; Engineer robust, high-quality software to enhance our vehicles' navigation capabilities and ensure safety on the road
Requirements: Possess a minimum of 4 years of hands-on experience in ML infrastructure or large-scale distributed systems; Demonstrate expertise in ML fundamentals and deep learning frameworks like PyTorch, Jax, Tensorflow, and Keras, with a focus on scalability, optimizations, and profiling; Proficient in Python and experienced in C++ development; Exhibit a strong passion for advancing robotics to improve everyday life experiences

Duties: Defining the metrics for tracking the health of our marketplace, and build tracking to monitor this; Proactive analysis and modeling to identify opportunities to further develop the marketplace, including innovating and building cutting-edge auction mechanics/pricing; Partnering closely with R&D and Sales stakeholders to develop the strategy for growing the marketplace, including the product roadmap and Sales programs to drive growth; Conducting deep dives on marketplace impact to support high impact launches and Sales growth
Requirements: Bachelor's degree in Statistics, Econometrics, Applied Math, or related fields; Strong academic or professional experience with the latest auction theory; Strong knowledge of Python 3 and SQL; Entrepreneurial ownership and a deep desire to push the status quo; Strong and clear communication skills; Experience working with scaled marketplace and auction-driven businesses; Strong analytical/modeling experience tied to business outcomes

Duties: Design, prototype, implement, evaluate, optimize and monitor machine learning algorithms and related software systems to generate sports data and insights with high accuracy and low latency; Build, test, deploy and maintain production systems; Maintain and promote best practices for software development, including deployment process, documentation, and coding standards; Contribute to technical and product discussions, and share knowledge and ideas with colleagues across the company
Requirements: BSc or MSc or equivalent experience with a strong software engineering component; Experience with deep learning and some of the languages and technologies in our stack; Affinity for teamwork and collaboration with others to solve problems, share knowledge, and provide feedback

Duties: Shape, grow, and lead exceptional teams that deliver novel, strategic ML solutions for Rokts product offerings; Leverage your ML industry expertise to introduce new technologies and processes and drive innovation within Rokt’s product offerings; Lead the development and deployment of machine learning models for content. understanding, embeddings, recommender systems, and ranking; Develop and maintain a deep understanding of user behavior, preferences, and engagement patterns through the use of ML; Collaborate with cross-functional teams including product, engineering, design, and data science to identify and prioritize opportunities for machine learning; Provide technical leadership and mentorship to the machine learning team; Partner with business stakeholders to understand their needs and develop solutions that drive business outcomes
Requirements: You have a deep technical background in applied machine learning, including deep learning, computer vision, natural language processing, and data engineering; An eye for product and relentlessly putting the customer at the heart of your strategic decisions; Previous experience in building and leading ML teams in creating end-to-end machine learning pipelines, including algorithms and tooling; Nice-to-have: A published researcher with a strong track record of academic experience in ML and AI

Duties: Builds and leads a generative AI practice within CDSAi. This includes use case development and associated stakeholder management in various business areas. It also means talent development (both recruiting new talent and training existing data scientists), infrastructure and tooling (in collaboration with ML Ops and IT), education of stakeholders and community, vendor selection/collaboration and governance (in collaboration with model governance team and Legal); Managing various stakeholders during solution design and project execution to successfully create solutions and deploy them into full production. Organizational effectiveness (within a complex company like NYL), storytelling and product branding are as important as technical leadership; Utilizes advanced statistical/AI techniques to create high-performing predictive models and other solutions to address business objectives and client needs. Tests new statistical and machine learning analysis methods, software, and data sources ...
Requirements: Graduate-level degree with concentration in a quantitative discipline such as statistics, computer science, mathematics, economics, or similar; 8+ years of experience with predictive analytics using large and complex datasets; Substantial expertise in both parametric statistical modeling techniques (linear regression, GLM, survival analysis, time series, etc.) and non-parametric techniques (GBM, NN, NLP); Expertise in regularization techniques (Ridge, Lasso, elastic nets), variable selection techniques, feature creation (transformation, binning, high level categorical reduction, etc.), validation (holdouts, CV, bootstrap) and model performance measures (may need to create new ones)

Duties: The Department of Chemistry at the University at Buffalo, The State University of New York, invites applications for a faculty position in chemical biology at the level of Associate or Full Professor. Candidates with research interests focusing on the development and/or use of chemical tools, and approaches to study fundamental questions in biology relevant to human health and medicine are encouraged to apply. This position is part of a multi-faculty recruitment effort supported by the Office of the Provost under the Disciplinary Excellence initiative
Requirements: PhD in chemistry or related area and an active record of funding, publications and presentations at scientific conferences appropriate to rank

Duties: This role will be the key data science leader for building our generative AI practice, in collaboration with our head of ML Ops. The person in this role is expected to have prior experience with creating generative AI solutions and will need to attract others with that skills set. NYL’s CEO has expressed that he wants the company to be a leader in generative AI in our industry. Hence, this leader has to opportunity to help build out that vision and with it their responsibility. Since not every data science project may require generative AI, expert-level familiarity with regular statistical predictive modeling methodologies and practice is also essential
Requirements: Graduate-level degree with concentration in a quantitative discipline such as statistics, computer science, mathematics, economics, or similar; 8+ years of experience with predictive analytics using large and complex datasets; Substantial expertise in both parametric statistical modeling techniques (linear regression, GLM, survival analysis, time series, etc.) and non-parametric techniques (GBM, NN, NLP); Expertise in regularization techniques (Ridge, Lasso, elastic nets), variable selection techniques, feature creation (transformation, binning, high level categorical reduction, etc.), validation (holdouts, CV, bootstrap) and model performance measures (may need to create new ones)

Duties: Performs actuarial pricing for Group Benefits Solutions products, including Life, Accident, Long Term Disability, Short Term Disability, and Absence products and services. Works with Underwriters to develop proper rate adjustments based on client risk profiles, special plan designs, and considerations from profitability, market competitiveness, and market strategic recommendations. Performs case-specific risk assessments to develop appropriate premium rates for a given risk to support the market’s growth and profitability goals
Requirements: Bachelor's degree in Economics, Finance, Statistics, Mathematics or Actuarial Science (willing to accept foreign education equivalent) plus three (3) years of experience performing actuarial services for insurance or annuity products

Duties: Leads data analysis and modeling projects from project and sample design. Participates in business review meetings with internal and external clients to develop requirements and deliverables. Performs analyses and modeling to final reports and presentations, communicates results, and provides implementation support. Demonstrates to stakeholders how analytics can be implemented to maximize business benefits. Provides strategic consulting, assessments, and project scoping and prepares and presents analytical perspective. Drives the use of data-based decision-making and analytics by active internal partnership management, discovering business opportunities, and creating business value by executing high-priority projects. Leverages advanced statistical and machine-learning techniques to create high-performing predictive models and creative analyses to address business objectives and client needs. Leverages scientific approaches (experimental design) to verify the performance of algorithms ...
Requirements: Master's degree in Mathematics, Statistics, Data Science, Computer Science, Economics, Operations Research or related quantitative field (willing to accept foreign education equivalent) plus three (3) years of experience as a Data Scientist or performing predictive analytics within the financial services or insurance industry

Duties: Design, build and support benchmark-relative performance attribution tools using Aladdin as well as customized engines to assess investment decision; Create, and curate a library of quant tools for relative value analysis, liquidity premia determination, and asset allocation decisions; Contribute as an independent thinker in impactful research pieces combining fundamental and quantitative insights across public and private markets; Produce and curate presentations to communicate insights to colleagues and investors to elevate the discussion of key decisions impacting portfolios; Interact with the Asset Liability and risk teams to coordinate building a consistent platform and tools to inform investment decisions
Requirements: 5+ years of experience as a quantitative analyst or quant investor; Advanced degree in finance, economics, statistics, or related discipline; Able to work independently to identify key themes and convey findings in compelling written communications and presentations; Expertise in statistical and quantitative methods in macro-economics and investments pertaining to performance attribution, asset allocation, portfolio optimization, and robust estimation; Expertise in python or related programming language and competence in database languages; Experience working on an asset allocation group with prior insurance experience is a plus

Duties: This role will be the key data science leader for building our generative AI practice, in collaboration with our head of ML Ops. It initially reports directly to Glenn and may later be integrated in the data science area with the largest use cases. The person in the role is expected to have prior experience with creating generative AI solutions and will need to attract others with that skills set. NYL’s CEO has expressed that he wants the company to be a leader in generative AI in our industry. Hence, this leader has to opportunity to help build out that vision and with it their responsibility. Since not every data science project may require generative AI, expert-level familiarity with regular statistical predictive modeling methodologies and practice is also essential
Requirements: Graduate-level degree with concentration in a quantitative discipline such as statistics, computer science, mathematics, economics, or similar; 8+ years of experience with predictive analytics using large and complex datasets; Substantial expertise in both parametric statistical modeling techniques (linear regression, GLM, survival analysis, time series, etc.) and non-parametric techniques (GBM, NN, NLP)

Duties: We are seeking a highly motivated candidate, interested in statistical cancer genomics, for a position as a postdoctoral fellow. The position will take advantage of the InterMEL study, an international consortium effort that has generated a large primary melanoma tissue resource and multi-modal tumor-profiling (DNA sequencing, DNA methylation, RNA-seq, multiplexed tissue imaging, and spatial transcriptomics). An example of a potential project relates to identifying driver mutations within and across oncogenic pathways, and their association with epigenomic, transcriptomic output, and tumor microenvironment characteristics
Requirements: PhD degree in biostatistics, statistics, bioinformatics, or a related field; Solid methodological training in statistics, be comfortable working with large data sets, proficient in at least one of the statistical programming languages R/Matlab/Python and have experience working on Unix/Linux systems and basic shell scripting

Duties: Develop and nurture a culture of continuous improvement by building capability across IOPS in the areas of statistical thinking, process improvement, and kaizen; Coach and mentor a diverse team of statisticians and quality engineers in the execution of common strategic goals, driving measurable improvements; Identify gaps in capability across IOPS. Design, support and implement programs, including training, that increase organizational competency; Effectively works towards root cause, proactively identifies and implements strategies for process improvements, and develops impactful leading metrics
Requirements: To be considered for this role, you must hold a BS/BA degree and/or MS in Biostatistics, Applied Math, Mathematics, Statistics, Applied Statistics/Data Analytics or related field; 6+ years experience; Lean, six sigma, and statistical methodology experience/training

Duties: Performs actuarial pricing for Group Benefits Solutions products, including Life, Accident, Long Term Disability, Short Term Disability, and Absence products and services. Works with Underwriters to develop proper rate adjustments based on client risk profiles, special plan designs, and considerations from profitability, market competitiveness, and market strategic recommendations. Performs case-specific risk assessments to develop appropriate premium rates for a given risk to support the market’s growth and profitability goals
Requirements: Bachelor's degree in Economics, Finance, Statistics, Mathematics or Actuarial Science (willing to accept foreign education equivalent) plus three (3) years of experience performing actuarial services for insurance or annuity products