Данные и ML вакансии - гибрид

• Опыт применения Generative AI и LLM к задачам discovery.

Требования: умеешь писать чистый и понятный код на современном Python; хорошо ориентируешься в классическом ML и соответствующих библиотеках (scikit-learn, CatBoost);

– Опыт работы Data Scientist от пяти лет. – Уверенное владение Python или R для анализа данных и разработки алгоритмов.

Location: Taguig, National Capital Region Workplace: Hybrid Salmon Bank (Rural Bank) Inc., the banking arm of Salmon Group Ltd, is a technology-driven challenger bank in the Philippines operating under a BSP license. We combine the trust of a regulated financial institution with the speed and convenience of a modern banking experience. Here, product, risk, compliance, operations, technology, and customer experience work closely together to build and run a modern bank. We work with fewer layers, close collaboration, and a culture where every voice can make a difference. If you want to build reliable financial systems at speed, take on real operational complexity, and help raise banking standards across the Philippines, come build with us. ABOUT THE ROLE You'll own credit scoring across the full customer lifecycle — from application decisions to behavioral models that drive portfolio profitability. WHAT YOU'LL DO - Build and improve Application and Behavioral Scorecards that drive underwriting and collections decisions across live credit products. - Work within a regulated environment — prepare model documentation and defend modeling decisions to internal governance and external reviewers. - Collaborate with product, analytics, and engineering to translate model outputs into strategy changes and implementation specs. WHAT YOU'LL OWN - Own the full modeling cycle: data exploration, feature design, model build, validation, deployment specs, and post-production monitoring. - Track scorecard quality — AUC, KS, bad rate, stability index — and validate against real portfolio outcomes via NPV and backtesting. - Prepare model documentation and governance artifacts for regulatory review. - Shape credit strategy — your models directly influence approval, pricing, and collection decisions, not just inform them. WHAT MAKES YOU A STRONG FIT - 3+ years of hands-on experience building credit risk models — Application Scorecards and/or Behavioral Scorecards specifically (ECL/IFRS9-only experience does not substitute). - Experience with model audits, documentation, or regulatory review support. - Strong Python (pandas, scikit-learn) and SQL — you build models yourself, not just review others' work. - Experience covering the full lifecycle: data, feature engineering, validation, and production monitoring. WHAT WE OFFER WORK AND OWNERSHIP - Hybrid-first work environment with a modern office in Bonifacio Global City, Manila - Company-provided tools and equipment - Work with an international team combining global fintech experience with deep Philippine market knowledge - Direct access to decision-makers and a real voice in how we build for the Philippine market HEALTH AND TIME OFF - Medical insurance for you and your family - 22 vacation days, Philippine public holidays, and 15 sick days - 105 days of paid maternity leave and up to 14 days of paid paternity leave LEARNING AND TEAM EXPERIENCE - Opportunities to learn and share your expertise through internal expert meetups, external conferences, speaking opportunities, and industry publications - Regular team offsites, sports activities, and team events - High-performing teams can earn a dedicated beach house week in Southeast Asia We believe strong teams are built by people with different backgrounds, experiences, and points of view. Salmon is an equal opportunity employer, and we make hiring decisions based on skills, experience, and potential.

Location: Foster City, CA Workplace: Hybrid Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. ABOUT THE ROLE You'll be one of the founding data scientist on Replit's Growth team, working with Product and Engineering leadership across acquisition, activation and recommender systems, and helping shape how Replit experiments. This role is for someone who doesn't wait to be asked. You spot problems and opportunities before anyone else does, bring a strong point of view, and push it until it changes what we build. YOU WILL: - Own data science for major growth areas, partnering with PMs and engineers from problem framing and sizing through launch and readout - Make fast decisions trustworthy: we ship weekly and need a decision within days, but retention takes weeks to show up. You'll design the experiments and metrics that close that gap; for example, variance reduction (CUPED), sequential testing, and short-term proxy metrics validated against long-term retention and revenue - Help set the experimentation standards teams rely on for Replit's highest-traffic surfaces: how metrics and guardrails are defined, and the checks that catch a broken test before it misleads anyone - Use quasi-experimental designs (difference-in-differences, synthetic control, regression discontinuity) where randomization isn't feasible, such as pricing changes and regional launches - Help define the data science behind an emerging personalization and recommendations workstream: problem framing, offline and online evaluation, and measuring incremental lift - Model the economics of a freemium, usage-based product: credit consumption, conversion, and how pricing and limits change behavior - Develop user and project segmentation from behavioral and content signals (e.g., embeddings and clustering) so growth analysis reflects what people are actually building - Partner with Data Engineering on a shared metrics layer and experimentation tooling REQUIRED SKILLS AND EXPERIENCE - 6+ years in data science, with significant time on growth or experimentation for high-scale prosumer, PLG, or self-serve products - Data science experience at a fast-growing startup, where you built metrics, datasets, and experimentation practices without much existing infrastructure - Deep expertise in experimental design and statistical inference, including reaching reliable decisions when readout windows are short and the outcomes that matter are slow - Experience establishing experimentation standards or platforms adopted by multiple teams - Expert SQL and Python - A track record of analysis that changed what a team shipped, and of influencing senior Product and Engineering leaders - Heavy, practical use of AI tools to move faster in your own work PREFERRED QUALIFICATIONS - Comfortable building your own datasets in dbt; - Pricing, packaging, or monetization analysis for usage- or credit-based products - International or mobile growth experience - MS or PhD in statistics, economics, computer science, or another quantitative field BONUS POINTS - Experience with multi-armed bandits or adaptive experimentation - Familiarity with developer tools, AI products, or creator platforms This is a full-time role based in our Foster City, CA office with an in-office requirement of Monday, Wednesday, and Friday. Full-Time Employee Benefits Include: 💰 Competitive Salary & Equity 💹 401(k) Program with a 4% match (US Only) ⚕️ Health, Dental, Vision and Life Insurance 🩼 Short Term and Long Term Disability 🚼 Paid Parental, Medical, Caregiver Leave 🏝 Flexible Time Off (FTO) + Holidays 🚗 Commuter Benefits (In-Office & US Only) 📱 Monthly Wellness Stipend 🧑‍💻 Autonomous Work Environment 🖥 In Office Set-Up Reimbursement (In-Office Only) 🚀 Quarterly Team Gatherings ☕ In Office Amenities (In-Office Only) Want to learn more about what we are up to? - Self-driving Company https://replit.com/blog/self-driving-company - Replit Agent at Scale https://replit.com/blog/evaluating-and-improving-agent-at-scale - AI Adoption https://replit.com/blog/ai-adoption - Build Open-Source Apps https://replit.com/build/open-source-app-builder   Interviewing + Culture at Replit - Operating Principles https://blog.replit.com/operating-principles - Reasons not to work at Replit https://blog.replit.com/reasons-not-to-join-replit To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds.

Position Overview: Join our Central Analytics team to build high-quality data marts, tools, and AI-powered workflows that shape player experience and drive game growth. What You’ll Take On: Write and optimize complex SQL queries to support, experimentation, and model feature engineering across large-scale player datasets.

Управление командой разработки в продуктах систем класса DWH - распределение задач, найм менторинг и развитие подчиненных, контроль и ответственность за результат команды; Формирование технических решений;

Предстоит: Разрабатывать ETL-процедуры с использованием Python, PySpark, Hadoop, Airflow; Собирать данные из разных реляционных и нереляционных источников (Kafka, Redis, Hadoop);

Задачи: — Управление и валидация кредитных моделей — Управление рисками моделей Требования: — Опыт работы с нейронными сетями, машинным обучением, XGBoost — Знание принципов управления моделями и оценки рисков — Навыки валидации моделей и документации Условия: — Работа в гибридном формате в офисе в Сан-Франциско — Зарплата от $207,600 до $306,600 в год

— Строить тестовые наборы и метрики для ИИ-ассистентов и агентов. — Запускать регулярные прогоны и ловить регрессии при смене моделей и промптов.

— Проектировать и улучшать промпты для ИИ-ассистентов и агентов клиентов. — Подбирать модели и настройки под задачу.

Location: San Francisco, CA Workplace: Hybrid ABOUT US At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater. ABOUT THE ROLE We're looking for a Deployed Engineer to join our Professional Services team, working directly with enterprise customers to build reliable, production agents. You'll translate vague enterprise workflows into concrete software specs and guide engineering teams through the resulting solution, or build it for them. You might spend a week designing a customer's agent architecture, a few weeks co-building their evaluation pipeline, or a quarter embedded inside their team shipping alongside their engineers. You are someone who's built real AI systems for production and can defend the technical tradeoffs within them. KEY RESPONSIBILITIES - Advising: Agent architecture design, evaluation strategy review, and best-practice production guidance. - Building: Co-build with the customer's engineering team across the full Agent Development Lifecycle (ADLC) in outcome-scoped engagements. - Embedding: Serve as a deployed engineer inside the customer's team for extended engagements, operating as a de facto member of their org to ship agent systems directly. - Agent Engineering: ADLC end-to-end, architecture design, orchestration patterns, evals, custom conversational UIs, and production deployment. - Applied AI: Post-training, supervised fine-tuning, harness engineering, trace mining, model selection and evaluation methodology. REQUIREMENTS - 4+ years of software engineering experience with deep expertise in Python. TypeScript/JavaScript a plus. - 2+ years of hands-on experience building and shipping production agent systems. - Strong client-facing communication skills, with the ability to confidently articulate architectural decisions to technical stakeholders (engineers, architects, CTOs). - Strong experience with LangChain/LangGraph/Deep Agents or comparable frameworks, including multi-agent patterns and state management (short and long-term memory). - Deep familiarity designing and implementing evaluation methodologies for non-deterministic AI systems. - Comfortable operating across the full spectrum from advisory to embedded delivery. - Willing to travel up to 20% of the time. NICE TO HAVE - Exposure to dataset curation and post-training techniques (SFT, DPO, RLHF) on open-weight models using tools like Axolotl, Unsloth, Hugging Face transformers, or TRL. - Experience with trace mining to drive continuous improvement loops LOCATION San Francisco, CA COMPENSATION $150,000-$300,000 base + equity Looking to lead technical strategy across the customer journey from Proofs of Value (PoVs) to driving overall adoption? Please check out the Deployed Engineer listing focused on Account Strategy & Solutions. Compensation Philosophy: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

• 2+ года в Data Engineering / DWH. • Работа с большими объёмами данных.

– Исследовать и прототипировать новые методы контроля качества данных на наших данных. – Проектировать ML- и статистические контроли для выявления аномалий, дрейфа распределений, деградации свежести данных, нарушений сезонных паттернов и нетипичных комбинаций метрик.

Мы в поиске Senior / Middle+ ML-инженера на аналитический проект. • Опыт локального развертывания LLM-моделей (Llama-3, Qwen) и работы с форматами GGUF / FP8 / AWQ.

Мы в поиске Senior / Middle+ ML-инженера на аналитический проект. • Опыт локального развертывания LLM-моделей (Llama-3, Qwen) и работы с форматами GGUF / FP8 / AWQ.

Предстоит: Разрабатывать и развивать ML/AI-решения для продуктов HR Tech и платформы Alfa People; Самостоятельно проходить полный цикл работы над задачей: от формулировки проблемы и дизайна решения до внедрения и оценки эффекта;

Не того, у кого Kafka и ClickHouse просто стоят в разделе Skills, а инженера, который действительно работал с DWH, строил ETL/ELT-пайплайны, оптимизировал SQL и понимает, что происходит с данными от источника до витрины. Наш стек: Kafka, ClickHouse, PostgreSQL/MySQL, Airflow, Python/Java/Scala, Docker, Grafana, Prometheus.