Role Description Square's Sales organization is where we're placing our biggest bets, and the go-to-market (GTM) Data Science team sits at the heart of that growth engine. Reporting to the Sales Data Science lead, you'll own a mature subdomain of our Sales function and contribute across the full channel, alongside peers in Sales Data Science (DS) and the wider GTM Data Science org. The role is a rare combination: you'll be a trusted analytics leader who sales executives turn to for the ground truth on business performance, and a hands-on data scientist who solves big, ambiguous problems end to end. This is a DS team on the front lines of AI, one that uses agents daily to clear away the mundane and free up time for the high-impact, technically challenging work that moves the business forward. You'll grow here: there's no shortage of hard problems, plenty of sharp colleagues to learn from, and the leverage to scale your impact well beyond your own domain. You Will: - Own performance measurement for your subdomain: monitoring the metrics that matter (wins, new revenue, funnel conversion, rep productivity), reporting against plan, and presenting the story in business reviews with sales leadership. - Lead investigations into performance questions ("why is X down?", "how can we improve Y?"), decomposing KPIs from first principles and designing experiments to test what actually works. - Take on large, ambiguous projects autonomously: scoping the problem, finding the right data, thinking critically about what it can and can't tell you, and landing the results with stakeholders. - Own and evolve your subdomain's data foundation, and partner with Sales DS peers on shared metrics, attribution logic, and cross-cutting analyses. - Translate data into business context: telling the story behind the numbers, pushing back on hypotheses with evidence, and building trust with senior sales, finance, and operations leaders. - Work AI-natively: use agents throughout your workflow and pioneer new ways of applying AI to the hardest problems in the sales domain. - Build self-serve dashboards and datasets that let the sales org answer its own questions. - Raise the bar for the team's analytical rigor, communication, and AI-enabled ways of working. Qualifications - Minimum of 8 years of related experience with a Bachelor’s degree; or 6 years and a Master’s degree; or a PhD with 3 years experience; or equivalent experience. - Advanced SQL and strong Python; comfort owning the full stack from ETL to analysis to visualization. - Strong statistical foundations: experiment design, causal inference, funnel and cohort analysis, and forecasting; you can interpret results with rigor and explain the concepts behind them. - A track record of running large projects end to end with minimal direction; you're the person leadership trusts when the numbers matter. - Strong critical thinking: you know when to move fast and when to slow down on a problem with big consequences, and you pressure-test your own conclusions. - Exceptional communication: you can present performance to executives, translate technical nuance for non-technical partners, and write analysis documents that stand on its own. - Enthusiasm for building with AI, and curiosity about how far it can be pushed in data science work. - Curiosity about unfamiliar data and how sales organizations actually work, and the empathy to partner well with the people on the frontline. Requirements - Nice to have, but not required: Prior experience supporting sales, GTM, or revenue organizations. - Familiarity with fintech, payments, or the SMB merchant space. - We value strong fundamentals and curiosity over industry background; we'll teach you our domain. Benefits - Remote work. - Medical insurance. - Flexible time off. - Retirement savings plans. - Modern family planning.

Role Description Our client is building an AI-powered job matching platform used by tens of thousands of active users every month. Matching quality is at the core of the product. Behind it is an ML-powered ranking system that determines which jobs each candidate sees. We are looking for a Middle NLP Data Scientist to own and advance this ranking stack: fine-tuning transformer models, designing training data, and turning offline improvements into measurable production impact. What You'll Do: - NLP Reranking (Core Responsibility) - Design, fine-tune, and iterate on different NLP algorithms (bi-encoders, cross-encoders) for persona–job matching. - Work across the NLP model stack: bi-encoder embeddings, text-pair classification, and cross-encoder reranking. - Build training data for ranking models: LLM-based labeling and improvement of human-annotated datasets. - Own offline and online evaluation: metric design (nDCG, recall@k), experimentation, and monitoring model performance in production. - Feature Engineering & Signal Extraction - Design feature pipelines from unstructured text (job descriptions, application forms, candidate profiles) and structured fields. - Build reusable, well-documented features suitable for both offline training and online inference. - Identify new signals and data sources that measurably improve matching quality. - LLM-Based Extraction & Data Foundations - Use LLMs for structured extraction from noisy text: prompt design for JSON output, schema validation, and automatic repair. - Audit existing data sources and build training-ready datasets for ML models. - Define data requirements with engineers: logging standards, formats, quality metrics, and monitoring signals. - Collaboration & Production Ownership - Work closely with backend and ML engineers to bring models to production (batch and near–real-time), define inference APIs, and ensure observability. - Own the end-to-end lifecycle: from data and features, to models, to production impact. First 3 Months - Expected Results: - Full understanding of the existing matching pipeline and its failure modes. - An upgrade to the ranking logic - strong improvement of current bi-encoders / cross-encoders. - A defined offline↔online evaluation loop and a clear baseline to measure every ranking change against. - Tangible, measured improvements in ranking quality (e.g. nDCG@10, recall@k) versus the current baseline. Qualifications - 2+ years of experience in Data Science / ML with a focus on NLP. - Hands-on experience fine-tuning transformer models (BERT-family, sentence-transformers, cross-encoders / bi-encoders). - Solid understanding of text embeddings, semantic similarity, and reranking. - Experience building training datasets for NLP models (including synthetic / LLM-labeled data). - Strong Python skills and comfort with the PyTorch / HuggingFace ecosystem. - Experience taking ML models to production together with engineers. - Solid SQL knowledge and sound offline/online evaluation habits. Requirements - Experience with search, ranking, or recommender systems (retrieval, Learning-to-Rank, hybrid sparse+dense search, BM25 + embeddings). - Experience with vector databases / ANN indexes (Qdrant, FAISS, etc.) and search engines (Elasticsearch / OpenSearch). - Practical experience using LLMs in production (extraction, labeling, evaluation). - Familiarity with HR tech, ATS systems, or marketplace products. Benefits - Market salary. - 20 workdays/year paid vacation. - Full-time remote work with flexible working hours. - Fast-paced, product-driven environment with real ownership and autonomy. - Opportunity to shape architecture and tech stack in a VC-backed startup from the early stages. - Close collaboration with a strong product team (PM, Design, Growth). - A culture of transparency, minimal bureaucracy, and quick decision-making. - Smart, ambitious teammates who value impact over process.

Role Description We are looking for a talented, enthusiastic and dedicated person to join Bill.com’s Risk Strategy team. This role will report to the VP of Fraud Risk Strategy and will work closely with cross-functional teams to formulate fraud and credit risk management strategies and controls for payments across different channels. This person will be responsible for building and maintaining the risk strategies and collaborating with cross-functional teams to operationalize these risk strategies. This position requires a person who has experience with using data to inform insights, influence stakeholders on relative priorities, management, refining risk strategies and driving initiatives. Qualifications - 8 years of experience in risk management within the Fintech or financial services industries with an emphasis on fraud and credit risks and 3 - 5 years in management - Bachelor's degree; MS/MA/MBA degree preferred in Business, Economics, Finance, Analytics, Mathematics, or a related field - Strong knowledge of the fundamentals of risk strategy such as fraud detection and prevention - Adept at SQL queries, reporting and presenting findings; Proficiency in Excel and other visualization tools - Strong organization and time management skills and the ability to prioritize and manage multiple projects at once - Ability to collaborate effectively with Product Management, Engineering teams, Customer success and sales teams - Extensive knowledge and experience with defining, developing, and deploying risk management methodologies and models - Expertise in driving operational efficiencies and scale through ongoing policy and model optimization - Strong analytical abilities; familiarity with typical credit data, systems & technologies is highly valuable - Hands-on experience leveraging AI/ML tools to strengthen fraud strategy - Familiarity with AI-assisted analytics platforms (e.g., Python-based ML libraries, AutoML tools, or vendor AI solutions) - Demonstrated ability to evaluate and govern AI-generated outputs within a fraud risk context Requirements - Communicate and liaise effectively with the Compliance team and formulate the strategy for new product launches - Experience managing cross-functional, multi-stakeholder processes, communicating and influencing stakeholders at various levels across functional boundaries - Familiarity with AI-assisted analytics platforms Benefits - 100% paid employee health, dental, and vision plans (choose HMO, PPO, or HDHP) - HSA & FSA accounts - Life Insurance, Long & Short-term disability coverage - Employee Assistance Program (EAP) - 11+ Observed holidays and wellness days and flexible time off - Employee Stock Purchase Program with employee discounts - Wellness & Fitness initiatives - Employee recognition and referral programs - And much more

• Join a global enterprise SaaS organisation to help build and scale a data-driven marketing analytics capability. • Lead the development of standardised reporting, dashboards, and performance frameworks that provide a trusted view of marketing performance across regions, business units, and channels. • Drive the evolution from reactive reporting to proactive, insight-led analytics that supports executive decision-making. • Partner with cross-functional teams to improve reporting, data quality, governance, and self-service analytics capabilities. • Lead the development and delivery of executive dashboards, scorecards, and marketing performance reporting. • Ensure reporting is accurate, timely, standardised, and aligned to agreed KPIs. • Reduce manual reporting through automation, reusable reporting frameworks, and AI-enabled processes. • Support the development of a centralised analytics environment using Microsoft Fabric and Power BI. • Consolidate fragmented reporting into scalable, standardised dashboards and templates. • Develop and maintain a central marketing KPI and metrics dictionary. • Implement data validation processes and improve data quality across marketing systems. • Support data governance and standard tracking frameworks. • Enable self-service analytics by creating documentation, templates, and user training. • Analyse marketing performance trends and deliver actionable recommendations to stakeholders. • Provide insight across digital marketing, brand performance, and communications effectiveness. • Collaborate across global teams to establish consistent reporting standards and shared analytics capabilities.