• Design and maintain the core AI orchestration layers, centralized API gateways, and reusable frameworks (e.g., advanced RAG architectures, agentic frameworks) for company-wide consumption. • Collaborate directly with core engineering teams to embed production-ready generative AI and machine learning features into the Actian Data Intelligence Platform. • Establish strict guardrails, evaluation frameworks, and monitoring tools to track model performance, bias, data privacy, and security across all AI implementations. • Actively monitor and manage cloud and API compute spend (token management, open-source vs. commercial models) and optimize execution latency for production AI features. • Lead workshops, design blueprints, and create documentation to empower non-AI engineering teams to build and maintain their own AI-driven features confidently. • Drive the engineering execution of high-impact AI proof-of-concepts, ensuring they are built with production-grade code that scales seamlessly. • Define and enforce the organization's official AI stack, from vector database selection and vector embeddings strategies to semantic caching mechanisms. • Evaluate and manage partnerships with AI model providers and lead the technical assessment of cutting-edge open-source models to keep Actian at the vanguard of innovation. • Define and track operational metrics for the AI Enablement function, such as developer adoption rates, reduction in time-to-market for AI features, and ROI of implemented AI tools.

• Design causal inference and quasi-experimental approaches for pricing and conversion measurement across our products • Build and calibrate probability and conversion models that feed optimization routines across our marketplace, distribution, and pricing products • Build elasticity and gain/loss models to power bidding and pricing optimization decisions • Select and validate statistical methods that best fit the data and business problem at hand • Build ranking and relevance models for sort order and personalization across our flight and hotel search products • Partner with engineering to productionize models, and with analytics and product teams to translate business questions into measurable, testable hypotheses

Role Description We are seeking a Data Scientist II (DS-2) to join our core data science team. In this role, you will build and ship models that power Mrsool's quick-commerce marketplace, owning well-scoped problems end-to-end — from analysis and experimentation through to production. You will work closely with cross-functional teams and senior data scientists to deliver robust, data-driven solutions. This position offers an opportunity to grow your craft on high-impact problems and contribute directly to the growth and success of the organization. What You Will Do - Marketplace Modelling: Build and maintain ML and optimisation models across the quick-commerce stack — supply-demand matching, dynamic and surge pricing, recommendations, ETA prediction, and broader marketplace optimisation. - Butler & Conversational AI: Contribute to the AI behind Butler, Mrsool's distinctive conversational ordering experience — modelling customer intent from free-form, unstructured requests (text, voice, images) and mapping it to fulfillable, well-priced orders. - Experimentation & Causal Inference: Design and run experiments (A/B and quasi-experimental) across pricing, matching, recommendations, and Butler, and turn noisy marketplace data into decisions stakeholders can act on. - Feature Engineering & Data Craft: Engineer high-signal features from messy, real-world data — order events, courier traces, geospatial signals, pricing configs, and conversational text/voice — as a core, ongoing part of the role. - Production ML: Own your models through their lifecycle — data pipelines, training, deployment, monitoring, and retraining — and respond when a model or config drifts. - Cross-Functional Collaboration: Collaborate effectively with product managers, engineers, DevOps, operations, and other squads to deliver seamless, data-driven experiences and to help diagnose live issues (e.g. mispriced brackets, elevated failure rates in a city). - Operational Excellence: Proactively monitor model and metric health, instrument your work with proper logging and observability, and contribute to reliable, repeatable analysis and deployment practices. - Continuous Improvement: Identify opportunities to improve measurement, modelling, and process; favour small, incremental changes that compound over time. Qualifications - Years of Experience: 3 to 4 years of non-internship professional data science or ML experience in fast-paced product startups or high-scale tech enterprises. - Experimentation & Causal Inference: Solid command of A/B test design, power analysis, and quasi-experimental methods (diff-in-diff, instrumental variables, synthetic control), including awareness of interference in marketplace/network settings. - ML & Optimisation Depth: Strong grounding in forecasting and at least one of operations research / reinforcement learning applied to allocation, matching, or pricing problems. - Feature Engineering: Proven ability to build, select, and maintain features from large, messy, real-world data. - Production Engineering: Comfortable deploying, monitoring, and maintaining ML pipelines, with the engineering discipline to keep models reliable in production. - Technical Toolkit: Fluent in Python and SQL, with the ability to work efficiently against large-scale data. - Problem-Solving Mindset: A knack for thinking from first principles and a track record of delivering high-quality work while balancing trade-offs like reliability, latency, and interpretability. - Iterative Mindset: A bias towards shipping early and iterating; a belief in small, incremental changes over large, multi-quarter undertakings. - Education: Bachelor's/Master's degree in Computer Science, Statistics, Engineering, or an equivalent quantitative field. Who Will Excel - Data scientists with hands-on experience in quick commerce, marketplaces, logistics, ride-hailing, or on-demand delivery, who understand two-sided supply/demand dynamics. - Those with NLP / LLM experience — intent classification, entity extraction, embeddings, or conversational/voice data — directly relevant to Butler. - Engineers comfortable with streaming/big-data tooling (Spark, Kafka) and real-time inference. - High-agency individuals who treat their models as products and collaborate well across conflicting perspectives. Benefits - Inclusive and Diverse Environment: We foster an inclusive and diverse workplace that values innovation and offers remote environments. - Competitive Compensation: Our compensation packages are highly competitive and include potential share options for certain roles. - Personal Growth and Development: We are committed to your personal and professional growth, providing regular training and an annual learning stipend to help you advance your career in a dynamic environment. - Autonomy and Mentorship: You'll enjoy a high degree of autonomy in your role, supported by mentorship and ambitious goals that pave the way for both your success and the company's growth.

Title: Junior Statistician - Surgery Location: Los Angeles United States Salary Range: $35.66 - 73.02 Hourly Job # : 29051 Job Description: Primary Duties and Responsibilities Press space or enter keys to toggle section visibility Drive Impactful Healthcare Research Through Data and Analytics The David Geffen School of Medicine at UCLA, Department of Surgery, is seeking a full-time Junior Statistician with a strong foundation in statistics and data science to join the Population-Health Research Core (PHRC) Biostatistical/Epidemiological Core. This role offers a unique opportunity to contribute to impactful research projects that shape the future of healthcare. As a key member of our fast-paced team, you’ll support transformative projects that shape healthcare delivery and policy through advanced statistical analysis and data-driven insights. You’ll collaborate with faculty and staff to manage and refine complex datasets, design study databases, and apply statistical modeling techniques to uncover meaningful patterns and outcomes. You’ll play a central role in developing visual tools—tables, graphs, and dashboards—that communicate findings clearly and effectively. We’re looking for someone who thrives in a collaborative environment, brings strong analytical skills, and is eager to learn and apply new statistical techniques. Join us in driving data-informed solutions to improve population health. Apply today and be part of a collaborative team at the forefront of healthcare research. Full hourly salary range is $33.96 - $69.54. Please note the department’s target hourly salary range is: $36.40 - $38.31. Job Qualifications Press space or enter keys to toggle section visibility Required: ·Bachelor’s degree in Statistics/Computer Science/Bioinformatics/Data Specification/ Data Science/Computational or equivalent combination of education and experience. ·1-2 years of experience in Statistics/Computer Science/Bioinformatics/Data Specification/Data Science. ·Knowledge of basic and multivariate statistics. ·Knowledge of research design. ·Competency in statistical software applications (e.g., R, SAS, STATA). ·Ability to conceptualize and construct tables and graphs. ·Working knowledge of statistical and/or medical terminology. ·Ability to summarize statistical analyses for collaborators. ·Knowledge of statistical techniques. ·Ability to utilize database management software to run queries and extract data. ·Ability to construct databases. ·Ability to produce visual tools and dashboards that highlight the essential information from a project or study. ·Knowledge of basic office software and applications (e.g., Microsoft Office Suite, Google, Internet search engines, etc.). ·Ability to learn new statistical techniques and to adapt existing code to customize statistical analysis strategies. As a condition of employment, the final candidate who accepts an offer of employment will be required to disclose if they have been subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct; or have filed an appeal of a finding of substantiated misconduct with a previous employer.