Senior Remote Data Scientist – Machine Learning, Predictive Modeling & Cloud Analytics at arenaflex

About arenaflex

arenaflex is a leading innovator in the retail pharmacy and health‑care ecosystem, operating thousands of community locations across the United States, Puerto Rico, and the U.S. Virgin Islands. With a heritage that spans more than a century, arenaflex has transformed from a traditional pharmacy chain into a technology‑driven health services platform. By blending deep industry expertise with cutting‑edge data science, arenaflex empowers millions of customers daily to access personalized health solutions, both in‑store and through a seamless omnichannel experience. Our mission is to create happier, healthier lives by leveraging data‑powered insights that drive better health outcomes, improve operational efficiency, and foster community well‑being.

Role Overview

We are seeking a highly motivated, senior‑level Remote Data Scientist to join arenaflex’s advanced analytics team. In this role, you will apply sophisticated data‑science, predictive‑analytics, and machine‑learning techniques to generate actionable insights, support strategic initiatives, and provide consulting guidance for product development. You will work closely with cross‑functional partners—including finance, engineering, product management, and business leadership—to translate massive data sets into clear, data‑driven recommendations that shape the future of health‑care delivery at arenaflex.

Key Responsibilities

• Design, develop, and deploy scalable machine‑learning models using advanced statistical methods, probability theory, and quantitative techniques.
• Interpret the business context behind large‑scale data, extracting meaningful patterns and delivering high‑impact analytical solutions.
• Apply rigorous statistical analysis to massive data sets, employing predictive models, customer segmentation, survey design, and data mining to uncover hidden opportunities.
• Engineer end‑to‑end data pipelines and analytical tools using Python, PySpark, Matplotlib, TensorFlow, PyTorch, and related open‑source libraries.
• Implement both supervised and unsupervised learning algorithms—decision trees, regression, XGBoost, K‑means clustering, anomaly detection, Bayesian methods, and interpretable ML—to address diverse business use cases.
• Leverage cloud platforms (Azure, Databricks) and data‑warehouse technologies (Snowflake) to build robust, production‑grade ML services.
• Adopt modern software‑engineering practices including GitHub version control, CI/CD pipelines, and agile development methodologies.
• Collaborate with finance analysts, data engineers, product owners, and senior business leaders to define product requirements, prioritize initiatives, and deliver analytical support.
• Communicate complex technical concepts clearly to non‑technical stakeholders, crafting compelling narratives that drive decision‑making.
• Mentor junior data scientists and contribute to a culture of continuous learning, knowledge sharing, and methodological rigor.

Essential Qualifications

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related STEM field, plus a minimum of four years of professional experience in data science, machine learning, or quantitative analysis. OR High School/GED with at least seven years of relevant experience.
• Advanced degree (M.S. or Ph.D.) in a quantitative discipline such as Computer Science, Statistics, Physics, or Mathematics is strongly preferred.
• Proven experience (≥4 years) working with large‑scale, complex data sets to build, optimize, and operationalize machine‑learning and predictive models.
• Expertise in SQL, Python, and PySpark (or comparable languages) for data manipulation, feature engineering, and model development.
• Demonstrated ability to conduct exploratory data analysis, feature selection, pattern detection, distribution analysis, and data visualization to inform business strategy.
• Hands‑on experience building classification models, decision trees, and ensemble methods (e.g., XGBoost).
• Solid foundation in both supervised (linear/logistic regression, time‑series modeling, SVMs) and unsupervised learning (K‑means, hierarchical clustering, association rules, PCA).
• Experience with cloud‑based ML platforms, distributed computing, data pipelines, and serving layers (Azure ML, Databricks, Snowflake).
• Track record designing and analyzing A/B experiments, translating results into actionable product recommendations.
• Exceptional communication skills—ability to convey rigorous technical concepts to non‑technical audiences and influence cross‑functional teams.
• Demonstrated success operating in fast‑paced, ambiguous environments, prioritizing tasks, and delivering high‑quality results on schedule.
• At least two years of experience influencing business decisions through data‑driven insights.
• Leadership experience (direct or indirect) managing project teams, mentoring peers, and driving strategic initiatives.
• Willingness to travel up to 10 % of the time for on‑site business engagements, both domestically and internationally.

Preferred Qualifications

• Ph.D. in a quantitative field such as Computer Science, Statistics, Physics, Mathematics, or Data Science.
• Experience with Internet of Things (IoT) data streams and Edge AI deployments.
• Background in Reinforcement Learning and its application to real‑world business problems.
• Domain expertise in health‑care, pharmacy operations, or related life‑science industries.

Core Skills & Competencies

• Technical Proficiency: Advanced programming in Python, PySpark, SQL; familiarity with ML frameworks (TensorFlow, PyTorch, Scikit‑learn).
• Statistical Acumen: Deep understanding of probability, hypothesis testing, Bayesian inference, and experimental design.
• Cloud & Big Data: Hands‑on experience with Azure, Databricks, Snowflake, and containerized deployment (Docker, Kubernetes).
• Data Engineering: Ability to build robust ETL pipelines, manage data quality, and ensure reproducibility.
• Business Insight: Strong sense of commercial impact, translating analytical findings into strategic recommendations.
• Communication: Clear, concise storytelling with both technical and executive audiences; strong documentation skills.
• Collaboration: Proven teamwork across multidisciplinary groups, fostering inclusive and innovative environments.
• Continuous Learning: Passion for staying current with emerging ML techniques, industry trends, and best practices.

Career Growth & Learning Opportunities

arenaflex invests heavily in the professional development of its data‑science talent. As a senior data scientist, you will have access to:

• Mentorship from industry‑leading experts and a clear pathway to principal or director‑level analytics roles.
• Sponsored certifications (e.g., Azure Data Scientist Associate, TensorFlow Developer) and attendance at premier conferences such as NeurIPS, KDD, and Strata Data.
• Opportunities to lead high‑visibility, cross‑functional projects that directly influence corporate strategy and product roadmaps.
• Rotational programs that expose you to other domains within arenaflex—such as supply chain optimization, digital health platforms, and customer experience design.
• Access to an internal learning hub featuring curated courses on advanced statistics, deep learning, MLOps, and ethical AI.

Work Environment & Culture at arenaflex

Our remote‑first culture is built on trust, flexibility, and a shared commitment to innovation. arenaflex encourages:

• Inclusive Collaboration: Diverse teams that value each member’s perspective, fostering creativity and better problem‑solving.
• Work‑Life Harmony: Flexible schedules, generous PTO, and a supportive environment that respects personal commitments.
• Health & Well‑Being: Comprehensive wellness programs, virtual fitness classes, and mental‑health resources.
• Community Impact: Volunteer initiatives and partnerships that allow employees to give back to the communities we serve.
• Innovation Labs: Dedicated time for exploratory projects, hackathons, and research collaborations with academic institutions.

Compensation, Perks & Benefits

• Competitive base salary commensurate with experience and market benchmarks.
• Performance‑based annual bonuses tied to individual and company success.
• Company‑paid life insurance, medical, prescription, dental, and vision coverage.
• Retirement savings plan (401 k) with employer matching contributions.
• Employee Stock Purchase Plan (ESPP) offering discounted shares.
• Generous paid time off (PTO) and paid parental leave (PPL).
• Transportation benefit plan and employee store discount for in‑person purchases.
• Voluntary life and personal accident insurance options.
• Access to cutting‑edge technology, cloud resources, and a collaborative virtual workspace.

How to Apply

If you are passionate about turning data into strategic advantage, thrive in a remote‑first environment, and want to make a tangible impact on the health of millions, we want to hear from you. Submit your application through the link below, and include a resume and a brief cover letter highlighting your most relevant experience and why arenaflex is the right next step for your career.

Apply Now – Join the arenaflex Team!

Closing Statement

At arenaflex, data science is not just a function—it’s a catalyst for transformation. Join us to shape the future of health‑care, collaborate with visionary leaders, and grow your expertise in a supportive, forward‑thinking environment. Your analytical talent can help us deliver happier, healthier lives to every community we serve. Apply today and become a pivotal part of our data‑driven journey.