Role Description We are looking for a senior machine learning expert and data analyst to help us design, extend, and operate financial risk scoring systems at scale. You’ll work on models and pipelines that process hundreds of terabytes of data and power decisions where accuracy, explainability, and robustness matter. This role sits at the intersection of machine learning, fintech analytics, and big-data engineering. You’ll help evolve our scoring algorithms, improve signal quality, and ensure our models remain reliable and interpretable in production environments. We’re especially interested in someone who can combine strong ML theory, hands-on data engineering, and pragmatic fintech experience. What You’ll Do - Design and improve financial risk scoring algorithms and models. - Analyze large-scale datasets (hundreds of TBs in Elasticsearch and related systems). - Build and maintain data processing pipelines for feature generation, training, and evaluation. - Develop ML models for anomaly detection, fraud detection, credit/risk scoring, and behavioral analysis. - Validate models for accuracy, bias, stability, and drift over time. - Ensure models are explainable, auditable, and production-ready. - Work closely with engineering teams to deploy models into production systems. - Optimize performance and cost across large-scale data infrastructure. - Define metrics, dashboards, and monitoring for model performance. - Investigate edge cases and failure modes in scoring systems. Qualifications - Senior-level experience in machine learning and data analysis (5+ years). - Strong background in financial risk, fintech analytics, or fraud detection. - Experience building and deploying production ML models. - Strong Python ecosystem skills (NumPy, pandas, scikit-learn, PyTorch/TensorFlow, etc.). - Experience with large-scale data processing (100s of TBs). - Deep experience with Elasticsearch or similar distributed data stores. - Experience designing data pipelines (batch and/or streaming). - Strong statistical reasoning and experimentation skills. - Ability to translate business risk concepts into measurable model features. - Experience evaluating model drift, bias, and long-term stability. Requirements - Experience with real-time scoring systems. - Experience with distributed compute frameworks (Spark, Beam, Flink, etc.). - Familiarity with regulatory or compliance-sensitive environments. - Experience with graph-based risk models or transaction network analysis. - Experience building internal analytics tools or dashboards. - Knowledge of feature stores and model versioning systems. Benefits - Competitive salary + performance incentives. - Equity aligned with long-term growth. - High ownership and direct exposure to leadership. - Remote-first with global team. - Health and sports benefits. - Yearly international team off-sites. - Work on high-impact financial risk systems. - Tackle real-world ML challenges at large scale. - Influence the architecture of our scoring and analytics platform. - Collaborate with experienced engineers and data specialists. - Own meaningful parts of our data and ML strategy. How to Apply Send us: - A short introduction and relevant experience. - Examples of ML or risk models you’ve worked on. - Links to projects, papers, or code (if available). We’re particularly interested in candidates who can demonstrate experience building robust financial risk models on very large datasets and bringing them successfully into production.

Role Description We’re looking for a Senior Data Engineer with a specialty in MLOps Engineering that can help drive the organization toward model development and delivery best practices. You will help shape and implement automation across the machine learning lifecycle from data collection to model training to model monitoring. In this high impact role, you will partner with both data engineers focused on data science service delivery and data scientists to develop a robust platform that shortens the time to market of new data science models at Quanata. Your day-to-day - Operationalize key data science solutions that enable risk‑prediction products across underwriting, pricing, claims routing, and marketing. - Design and build ML pipelines using industry best practices, primarily leveraging AWS services like SageMaker, and integrating with tools such as MLflow for experiment tracking and data platforms like Snowflake. - Stand‑up and operate a shared feature store (Snowflake Snowpark + Kafka) that supports both batch and real‑time feature retrieval. - Own real‑time inference services, exposing low‑latency endpoints (SageMaker endpoints or EKS micro‑services) and managing blue/green or canary deployments. - Implement comprehensive testing strategies (including Unit, integration, data validation, model validation, and performance testing) within robust CI/CD pipelines to maintain high platform quality. - Enable ML Governance: Manage ML models and data versioning, experiment tracking, and reproducibility. - Implement event‑driven orchestration that triggers automated retraining, evaluation, and redeployment based on data drift or business events. - Monitor production models for performance, drift, and data quality—and drive automated remediation. Qualifications - Bachelor degree or equivalent relevant experience. - 8 years of industry experience with 2 years focused in MLOps and 2 years in software engineering or equivalent experience. - Comprehensive experience in Python and Docker. Familiarity with build tooling such as Bash and Bazel. - Advanced proficiency in IaC principles and tools like Terraform. - Demonstrated expertise in designing, deploying, and managing scalable and resilient MLOps solutions on AWS. - Applied expertise in the end-to-end machine learning lifecycle, including data ingestion, preprocessing, model training, deployment, and production monitoring. - Excellent written and verbal communication with a strong collaborative focus. - Proficiency in designing and implementing workflows using tools like AWS Step Functions. - Experience with CI/CD tailored for machine learning systems (e.g., automating model training, validation, and deployment). Bonus points - Experience in designing and developing large-scale distributed systems, complex APIs, or contributing significantly to platform-level software engineering projects. - Proficiency in utilizing Snowflake's advanced capabilities for ML, such as Snowpark for Python/Java/Scala development, creating and managing user-defined functions (UDFs) for in-database scoring, or integrating directly with external model training and serving platforms. - Prior experience working within the insurance industry or another highly regulated environment, demonstrating an understanding of pertinent regulatory, security, and data governance challenges. Salary $213,000 to $300,000* *Please note that the final salary offered will be determined based on the selected candidate's skills, and experience, as well as the internal salary structure at Quanata. Our aim is to offer a competitive and equitable compensation package that reflects the candidate's expertise and contributions to our organization. Benefits - We provide a wide variety of health, wellness and other benefits. These include medical, dental, vision, life insurance and supplemental income plans for you and your dependents, a Headspace app subscription, monthly wellness allowance and a 401(k) Plan with a company match. - Given our virtual environment— in order to set you up for success at home, a one-time payment of $2K will be provided to cover the purchase of in-home office equipment and furniture at your discretion. Also, our teams work with MacBook Pros, which we will deliver to you fully provisioned prior to your first day. - All employees accrue four weeks of PTO in their first year of employment. New parents receive twelve weeks of fully paid parental leave which may be taken within one year after the birth and/or adoption of a child. The twelve weeks is applicable to both birthing and non-birthing parent. - We’re committed to investing in and helping our people grow personally and professionally. All employees receive up to $5000 each year for professional learning, continuing education and career development. All team members also receive LinkedIn Learning subscriptions and access to multiple different coaching opportunities through BetterUp. Location We are a remote-first company for most positions so you may work from anywhere you like in the U.S, excluding U.S. territories. For most positions, occasional travel may be requested or encouraged but is not required. Some positions might require travel per the job description provided to the employee. Employees based in the San Francisco Bay Area or in Providence, Rhode Island may commute to one of our local offices as desired. Hours We maintain core meeting hours from 9AM - 2PM Pacific time for collaborating with team members across all time zones. Equal Opportunity Statement Quanata, LLC is an equal opportunity workplace. We are committed to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

• Build production models, designing, training, and deploying production-grade deep learning models for critical perception tasks. • Advanced Edge optimization including fine-tuning models for real-time inference on embedded NVIDIA GPU platforms. • Own model development from rapid prototyping through to validation and final deployment. • Collaborate closely with robotics team to integrate perception outputs into the robotics stack. • Analyze performance on mowing site data, identify failure cases, and continuously improve models. • Develop strategies for using real and synthetic data to train models and validate performance.

• design, build, and operationalize scalable, secure, and responsible Generative AI solutions • drive the development and implementation of advanced machine learning models • collaborate with multiple departments to understand business requirements • interface with stakeholders to define performance metrics and evaluation methodologies • design and implement scalable machine learning systems • advise on optimization and improvement of data pipelines and model training processes • consult on and present technical findings to stakeholders • ensure compliance with data privacy regulations • manage team performance through feedback and performance reviews • stay up-to-date with the latest advancements in machine learning and technologies