About Airwallex Airwallex is the only unified payments and financial platform for global businesses. Powered by our unique combination of proprietary infrastructure and software, we empower over 200,000 businesses worldwide - including Brex, Rippling, Navan, Qantas, SHEIN and many more - with fully integrated solutions to manage everything from business accounts, payments, spend management and treasury, to embedded finance at a global scale. Proudly founded in Melbourne, we have a team of over 2,000 of the brightest and most innovative people in tech across 26 offices around the globe. Valued at US$8 billion and backed by world-leading investors including T. Rowe Price, Visa, Mastercard, Robinhood Ventures, Sequoia, Salesforce Ventures, DST Global, and Lone Pine Capital, Airwallex is leading the charge in building the global payments and financial platform of the future. If you're ready to do the most ambitious work of your career, join us. Attributes We Value We hire successful builders with founder-like energy who want real impact, accelerated learning, and true ownership. You bring strong role-related expertise and sharp thinking, and you're motivated by our mission and operating principles. You move fast with good judgment, dig deep with curiosity, and make decisions from first principles, balancing speed and rigor. You're humble and collaborative; turn zero-to-one ideas into real products, and you "get stuff done" end-to-end. You use AI to work smarter and solve problems faster. Here, you'll tackle complex, high-visibility problems with exceptional teammates and grow your career as we build the future of global banking. If that sounds like you, let's build what's next. About the team As a global team, we span across Australia, China, USA, and Singapore, revolutionizing applied data science, data engineering and platform solutions to support Airwallex's rapid growth. You will collaborate with a diverse range of cross-functional partners, including Product, Engineering, Marketing, Sales, Finance, and more, to tackle complex data problems and shape the future of fintech. Learn more about the data science team in this blog. This role is based in Singapore. Responsibilities: - Define, understand, and test opportunities and levers to improve the product, and drive roadmaps through your insights and recommendations - Work with large and complex data sets to solve a wide array of business problems using different analytical and statistical approaches - Apply technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for our products - Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends - Partner with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions Who you are We're looking for people who meet the minimum qualifications for this role. The preferred qualifications are great to have, but are not mandatory. Minimum qualifications: - Minimum 6 years of industry experience and a degree in a quantitative field - Experience with communicating the results of analyses to executives and cross-functional teams to influence the strategy - Expert in data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R). Experience in schema design and dimensional data modeling a plus - Ability to perform statistical analysis to drive data-informed decisions Preferred qualifications: - Experience in technology, financial services and/or a high growth environment is advantageous Applicant Safety Policy: Fraud and Third-Party Recruiters To protect you from recruitment scams, please be aware that Airwallex will not ask for bank details, sensitive ID numbers (i.e. passport), or any form of payment during the application or interview process. All official communication will come from an @airwallex.com email address. Please apply only through careers.airwallex.com or our official LinkedIn page. Airwallex does not accept unsolicited resumes from search firms/recruiters. Airwallex will not pay any fees to search firms/recruiters if a candidate is submitted by a search firm/recruiter unless an agreement has been entered into with respect to specific open position(s). Search firms/recruiters submitting resumes to Airwallex on an unsolicited basis shall be deemed to accept this condition, regardless of any other provision to the contrary. Equal opportunity Airwallex is proud to be an equal opportunity employer. We value diversity and anyone seeking employment at Airwallex is considered based on merit, qualifications, competence and talent. We don't regard color, religion, race, national origin, sexual orientation, ancestry, citizenship, sex, marital or family status, disability, gender, or any other legally protected status when making our hiring decisions. If you have a disability or special need that requires accommodation, please let us know.

Role Description Guide research and engineering teams to close knowledge gaps in data science, AI, and machine learning domains. Surface nuances that distinguish expert-level work from surface-level reasoning. - Design challenging data science and AI tasks rooted in real-world statistical modeling, ML pipelines, data engineering, and software workflows. - Write accurate, well-documented solutions that serve as ground truth. - Evaluate AI agent outputs and data science solutions. - Provide detailed written feedback that captures correctness, efficiency, and reasoning quality. - Develop and refine evaluation frameworks and rubrics for assessing statistical reasoning, ML pipeline design, agentic behavior, and data analysis approaches. - Collaborate with other subject matter experts to ensure consistency and accuracy in training data. Qualifications - 3+ years of professional or research experience in data science, machine learning, data engineering, AI systems, or software engineering. - Demonstrated technical expertise in at least one of: statistical modeling, ML modeling, data analysis, programming, LLMs, or agentic workflows. - Demonstrable career progression. - Ability to commit to 40 hours per week during weekdays for the duration of the engagement. - Strong written communication skills and the ability to explain analytical and technical decisions clearly. Requirements - Prior experience with data annotation, labeling, evaluation, or human feedback collection is a strong plus. - Experience with LLMs, AI systems, or agentic workflows; familiarity with agentic frameworks is a plus. Benefits - W-2 employment with Cincinnatus LLC. - Paid weekly. Application Process - Upload resume. - AI interview based on your resume. - Submit form. Resources & Support - For details about the interview process and platform information, please check: Interview Process . - For any help or support, reach out to: support@mercor.com .

Role Description Dexcom is seeking a Senior Manager, Behavioral Science – Customer Success to lead the integration of behavioral science into the Customer Success ecosystem. This role is anchored within the Behavioral Science function and operates in close partnership with Technical Support / Customer Success to design, implement, and scale behaviorally informed support strategies aligned with enterprise engagement priorities. The Senior Manager will design and implement behaviorally informed strategies that: - Improve customer experience - Strengthen confidence during support interactions - Reduce friction across the support journey - Enhance engagement with Dexcom products following customer contact This role represents a strategic opportunity to translate behavioral insights into scalable operational improvements that benefit both customers and customer success teams. The position will work cross-functionally with: - Customer Success leadership - Product - Clinical Affairs - Customer Experience - Data Science partners This is an applied behavioral leadership role focused on implementation and impact at scale rather than traditional academic research. - Co-develop and execute a behavioral science strategy aligned with Customer Success priorities and the broader Behavioral Science roadmap supporting engagement, adoption, and sustained product use. - Identify behavioral drivers of customer confusion, disengagement, and repeat support contacts. - Translate behavioral insights into scalable improvements across support workflows, agent interactions, and customer journeys. - Design frameworks that strengthen customer confidence, trust, and product understanding during support interactions. - Develop behaviorally informed interventions that improve satisfaction and resolution effectiveness. - Partner with Customer Success leadership to reduce friction across high-impact customer touchpoints. - Strengthen post-interaction engagement strategies that support product adoption and continued use. - Identify opportunities to reinforce onboarding through support experiences. - Apply behavioral science principles to improve agent communication and decision-support approaches. - Inform training strategies that increase agent confidence and consistency in complex support scenarios. - Improve employee experience through behaviorally informed workflow enhancements. - Lead pilots and structured experiments to evaluate support interventions in partnership with Behavioral Science analytics teams. - Define behavioral and operational metrics linking support interactions to downstream engagement outcomes. - Measure impact across satisfaction, repeat contacts, activation behaviors, and product utilization in collaboration with analytics partners. - Serve as a strategic liaison between Behavioral Science and Customer Success leadership to align support initiatives with broader behavior-change efforts across Product, Clinical Affairs, and Customer Experience. - Partner cross-functionally to scale effective behavioral interventions. - Translate qualitative and quantitative insights into actionable operational recommendations. - Support integration of behavioral evidence into the broader customer experience strategy. - Use Behavioral Science research and analytics infrastructure to ensure consistent evaluation across customer engagement initiatives. Qualifications - Advanced degree (PhD, MS, MPH, or equivalent experience) in Behavioral Science, Psychology, Human Factors, Implementation Science, or related field - 8+ years of experience applying behavioral science in healthcare, digital health, customer experience, or service delivery environments - Demonstrated success translating behavioral insights into operational improvements at scale - Experience working cross-functionally with operations or service organizations - Strong stakeholder engagement and influence skills across technical and non-technical audiences - Experience designing and evaluating applied behavioral interventions Benefits - A front row seat to life changing CGM technology. Learn about our brave #dexcomwarriors community. - A full and comprehensive benefits program. - Growth opportunities on a global scale. - Access to career development through in-house learning programs and/or qualified tuition reimbursement. - An exciting and innovative, industry-leading organization committed to our employees, customers, and the communities we serve. Experience and Education Requirements - Typically requires a Bachelor’s degree in a technical discipline, and a minimum of 13+ years related experience or a Master’s degree and 8+ years equivalent industry experience or a PhD and 5+ years of experience. Travel Required - 15-25% Remote Workplace - Your location will be a home office; you are not required to live within commuting distance of your assigned Dexcom site (typically 75 miles/120km). - If you reside within commuting distance of a Dexcom site (typically 75 miles/120km) a hybrid working environment may be available. Ask about our Flex workplace option. Salary $164,200.00 - $273,600.00

Role Description We are hiring a Weather Data Scientist to advance the next generation of weather forecasting systems for India, with strong attention to observational data quality and geospatial consistency. You will work closely with machine learning and software engineers on three core threads: - Data assimilation: contribute hands-on to data assimilation for weather forecasting models. - ML-ready datasets: procure, process, and create ML-ready global and regional weather datasets at large scale (high volume, multi-source, long time horizons), with explicit focus on data-sparse regions. What you'll work on: - Build and operate a cycling data assimilation pipeline for our operational forecasting models, and produce the high-resolution gridded products it enables downstream. - Choose, deploy, and adapt a modern DA framework (e.g. JEDI/UFO, GSI, DART, PDAF) for our regional and global needs. - Develop observation quality control, bias correction (VarBC), and thinning workflows that hold up at operational data volumes and degrade gracefully when feeds drop out. - Contribute to AI-based data assimilation pipelines. - Tailor weather prediction models to renewable-sector needs, particularly solar (GHI) and wind generation (100m winds). - Assist in training AI-based weather prediction models. - Work at the intersection of physics-based modeling and machine learning—hybrid physics–ML systems, learned parameterizations, and emulators. Qualifications - A master's or PhD in geophysical sciences, physics, applied mathematics, computer science, statistics, or a related field. A bachelor's degree with 3+ years of relevant research or operational experience is also acceptable. - Demonstrated depth in data assimilation, evidenced by operational work, model contributions, research projects, publications, or technical reports. - Hands-on experience across the DA toolkit: observation operators and error specification; variational (3D-/4D-Var) or ensemble (EnKF, LETKF, EDA) methods; cycling workflows and innovation statistics; and assimilation of satellite, radar, radiosonde, or station observations. - Hands-on experience with at least one operational DA framework—JEDI/UFO, GSI, DART, PDAF, or an in-house equivalent—including building observation operators and forward models. - Working knowledge of bias correction (VarBC), adaptive QC, and gross-error rejection. - Experience contributing to or maintaining assimilation code, or holding responsibility in an operational or quasi-operational forecasting pipeline. - Experience working with TB-scale, high-dimensional observational and modeling datasets (reanalysis, satellite, radar, weather-station, and sounding data) and the geospatial pipework (grids, reprojection, masks) around them. - Hands-on experience with widely used reference datasets such as ERA5, MERRA-2, IMDAA, IMERG/GPM, and GOES/INSAT/Himawari. - Practical experience on High Performance Computers (HPCs). - Fluency in the modern geoscience Python stack—xarray, dask, zarr, netCDF. - Experience building reproducible, production-grade pipelines. - Excellent written and verbal communication, including the ability to explain technical work to both domain experts and cross-disciplinary collaborators. - Prior work on projects specific to Indian geography. - Familiarity with coupled earth-system models. - Experience with any of: ensemble and probabilistic forecasting, regional downscaling, or subseasonal-to-seasonal (S2S) prediction. - Experience working with operational forecasting agencies (IMD, NCMRWF, ECMWF, NOAA, etc.). - Familiarity with AI-based weather prediction models and data assimilation techniques. - Comfort using agentic AI tools to accelerate development. - Publications in respected atmospheric, oceanic, or climate science venues. Benefits - Part of development of weather forecasting models deployed for real-time applications. - Experience working on hard, open-ended problems at the intersection of AI and physical infrastructure. - Exposure to how teams set priorities and push the frontier of AI weather prediction. - Close collaboration with a deeply technical team. Company Description Pravāh is an AI lab building foundational intelligence for the electric grid. We apply modern machine learning to complex physical infrastructure problems spanning grid operations, weather, and geospatial systems. Our work sits at the intersection of computer vision, physical systems, and large-scale ML, with deployments across utilities in the United States and India. We leverage multimodal data including satellite imagery, LiDAR, and street-level data to build high-fidelity representations of grid assets and their surroundings. We are backed by Khosla Ventures, Pear VC, and Conviction - some of the most ambitious investors in Silicon Valley.