• Analyze large and complex datasets to uncover actionable insights and support data-driven decisions. • Develop, implement, and optimize machine learning models and algorithms using Python. • Leverage GCP Data and AI services to build scalable data solutions and predictive analytics. • Write efficient SQL queries and perform query optimization for data extraction and manipulation. • Collaborate with cross-functional teams to understand business challenges and translate them into analytical solutions. • Design and implement data pipelines to support machine learning processes. • Create clear and insightful data visualizations and reports for stakeholders. • Stay up to date with emerging trends and technologies in data science and AI.

What You'll Do As a Senior Data Scientist (AI Enablement & Solutions), you'll help accelerate our internal data organization by identifying high-value problems, evaluating rapidly evolving AI/ML tools, and delivering scalable solutions that improve the efficiency and quality of our analytics output. This role is intentionally problem-first: you'll start from the business outcome, define what "good" looks like, and then determine the most pragmatic technical path—often learning and adapting new data/AI capabilities along the way as tools evolve. You'll be expected to make strong judgment calls in ambiguous situations, run lightweight but rigorous evaluations, and ship maintainable internal data products and workflows that are trusted by stakeholders and sustainable for the team to operate long-term. #LI-Remote ***This role is not eligible for sponsorship** What Your Responsibilities Will Be Problem-First Solution Assessment & Technical Direction - Partner with stakeholders to translate our needs into clear problem statements, success metrics, and constraints (accuracy, latency, governance, cost, and usability). - Assess solution paths (build vs. buy vs. hybrid), and recommend an approach that maximizes value while remaining scalable and maintainable. AI Tooling Adoption & Enablement - Stay current on emerging AI/LLM capabilities and identify where they can materially improve analyst productivity, data product usability, or decision-making quality. - Define reference patterns for safe and reliable AI-enabled analytics (prompting patterns, retrieval strategies, evaluation approaches, monitoring/observability). - Create lightweight enablement assets (playbooks, templates, example implementations) that help the broader data team adopt new tools. Deliver AI-Enabled Data Products - Design and implement internal data capabilities such as self-serve analytics experiences, semantic/metric layers, AI-assisted workflows, or decision-support tooling. - Partner with data engineering to productionize solutions: APIs, pipelines, access controls, logging/telemetry, and CI/CD practices. - Ensure solutions are operable and maintainable: documentation, runbooks, ownership clarity, and measurable service expectations. Quality, Reliability, and Governance - Build guardrails that increase trust: correctness checks, permission-aware outputs, auditability, regression testing, and monitoring. - Establish evaluation frameworks appropriate for AI-enabled systems (golden datasets, offline metrics, human review loops, ongoing quality measurement). Traditional Data Science & Analytics Apply statistical and machine learning techniques to guide insights or enhance internal products (forecasting, segmentation, anomaly detection, experimentation support). What You'll Need to be Successful - 3+ years experience in data science, analytics engineering, data engineering, or applied ML roles with a track record of delivering productionized solutions. - "problem-first" mindset: demonstrated ability to start from an ambiguous business need, define success criteria, and deliver an end-to-end solution that partners adopt. - Experience learning new tools/frameworks/platforms to unlock the best path forward (rather than defaulting to familiar methods). - Hands-on ability to build and ship: SQL and Python; experience developing production workflows/services (APIs a strong plus). - Experience working with modern data warehouses and analytics stacks (Snowflake experience a plus). - Excellent communication and influence skills; comfortable driving alignment across technical and non-technical stakeholders. Preferred - Experience evaluating and implementing AI/LLM-enabled solutions (e.g., tool/platform selection, POCs, rollout, monitoring). - Familiarity with production best practices (testing, CI/CD, observability), and building maintainable systems. - Experience with semantic modeling / metric layers and analytics governance. Avalara is an AI-first Company AI is embedded in our workflows, decision-making, and products. Success here requires embracing AI as an essential capability. - You’ll bring experience using AI and AI-related technologies, ready to thrive here. - You’ll apply AI every day to business challenges - improving efficiency, contributing solutions, and driving results for your team, our company, and our customers. - You’ll grow with AI by staying curious about new trends and best practices, and by sharing what you learn so others can benefit too. How We'll Take Care of You Total Rewards In addition to a great compensation package, paid time off, and paid parental leave, many Avalara employees are eligible for bonuses. Health & Wellness Benefits vary by location but generally include private medical, life, and disability insurance. Inclusive culture and diversity Avalara strongly supports diversity, equity, and inclusion, and is committed to integrating them into our business practices and our organizational culture. We also have a total of 8 employee-run resource groups, each with senior leadership and exec sponsorship. What You Need To Know About Avalara We’re defining the relationship between tax and tech. We’ve already built an industry-leading cloud compliance platform, processing over 54 billion customer API calls and over 6.6 million tax returns a year. Our growth is real - we're a billion dollar business - and we’re not slowing down until we’ve achieved our mission - to be part of every transaction in the world. We’re bright, innovative, and disruptive, like the orange we love to wear. It captures our quirky spirit and optimistic mindset. It shows off the culture we’ve designed, that empowers our people to win. We’ve been different from day one. Join us, and your career will be too. We’re An Equal Opportunity Employer Supporting diversity and inclusion is a cornerstone of our company — we don’t want people to fit into our culture, but to enrich it. All qualified candidates will receive consideration for employment without regard to race, color, creed, religion, age, gender, national orientation, disability, sexual orientation, US Veteran status, or any other factor protected by law. If you require any reasonable adjustments during the recruitment process, please let us know.

Company Description At Northwestern Medicine, every patient interaction makes a difference in cultivating a positive workplace. This patient-first approach is what sets us apart as a leader in the healthcare industry. As an integral part of our team, you'll have the opportunity to join our quest for better health care, no matter where you work within the Northwestern Medicine system. We pride ourselves on providing competitive benefits: from tuition reimbursement and loan forgiveness to 401(k) matching and lifecycle benefits, our goal is to take care of our employees. Ready to join our quest for better? Job Description Job Title: Senior Data Scientist Location: Chicago, IL Compensation: $114,180 - $182,689 per year Responsibilities: Build models by executing predefined experiments and templated code within the standard model development paradigm (25%). Data extraction and preparation for use in solution building (20%). Lead requirements gathering efforts using your technical and product acumen to identify solutions to stakeholder problems (25%). Provide technical and infrastructural advice on model builds, solution design, and requirements (15%). Help train the rest of the team in product lifecycle management, solution design, technical tool usage, and machine learning specifics (15%). ***Remote work requests will be considered consistent with petitioner's remote work policy. Requirements: Master’s degree in Machine Learning, Computer Science, Engineering, Math, Physics, or a related field plus 5 years of relevant experience with machine learning, a focus on model building, and analysis of outcomes and model deployment in Data Scientist- Innovation, Graduate Assistant, or a related position. In alternative, we will accept a Doctorate (Ph.D.) in Machine Learning, Computer Science, Engineering, Math, Physics, or a related field plus 3 years of relevant experience with machine learning, a focus on model building, and analysis of outcomes and model deployment in Data Scientist- Innovation, Graduate Assistant, or a related position. Additionally, we require: experience running projects from inception to deployment; familiar with the product development lifecycle; proficiency in SQL, Python, and a deep learning library like PyTorch; experience with machine learning algorithms and frameworks like scikit-learn (sklearn) or SparkML for building and evaluating models; professional experience using cloud platforms like Azure for scalable data processing and model deployment; experience with software engineering principles around testing, code reviews, and deployment; experience utilizing problem-solving skills to analyze complex problems, break them down into manageable tasks, and develop effective solutions; experience working effectively in interdisciplinary teams, including clinicians, to understand their needs and translate them into data-driven solutions; experience leading and influencing cross-functional teams; experience building and growing relationships with stakeholders to ensure alignment and successful implementation of data-driven solutions; experience with feature engineering and selection methods to improve model performance; experience mentoring on topics like programming, deployment, maintenance, project lifecycle, and working with stakeholders. We will accept any suitable combination of education, training, and experience. Requisition # LMT90751K Qualifications Additional Information All your information will be kept confidential according to EEO guidelines. Northwestern Medicine is an equal opportunity employer (disability, VETS) and does not discriminate in hiring or employment on the basis of age, sex, race, color, religion, national origin, gender identity, veteran status, disability, sexual orientation or any other protected status. Background Check Northwestern Medicine conducts a background check that includes criminal history on newly hired team members and, at times, internal transfers. If you are offered a position with us, you will be required to complete an authorization and disclosure form that gives Northwestern Medicine permission to run the background check. Results are evaluated on a case-by-case basis, and we follow all local, state, and federal laws, including the Illinois Health Care Worker Background Check Act. Artificial Intelligence Disclosure Artificial Intelligence (AI) tools may be used in some portions of the candidate review process for this position, however, all employment decisions will be made by a person. Benefits We offer a wide range of benefits that provide employees with tools and resources to improve their physical, emotional, and financial well-being while providing protection for unexpected life events. Please visit our Benefits section to learn more. Sign-on Bonus Eligibility (if sign-on bonus offered for position): Internal employees and rehires who left Northwestern Medicine within 1 year are not eligible for the sign on bonus. Exception: New graduate internal employees seeking their first licensed clinical position at NM may be eligible depending upon the job family. - Job Shift: Day Job (1st) - Salary Range Minimum : $54.90 - Salary Range Maximum: $87.84 - Compensation: USD 54.9 - USD 87.84 - hourly

• Lead the development of advanced analytics and machine learning solutions • Transform complex data into actionable insights • Build predictive models and deliver scalable data science solutions • Collaborate with cross-functional stakeholders across product, engineering, and business teams