Senior Data Scientist – Supply Chain Optimization & Omnichannel Strategy – Remote – arenaflex

About arenaflex Welcome to arenaflex, a global technology powerhouse that is redefining the future of retail and logistics. With a mission to empower millions of customers worldwide, arenaflex blends cutting‑edge data science, machine learning, and advanced analytics with a deep understanding of supply‑chain dynamics. Our culture is built on curiosity, collaboration, and a relentless drive to solv
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e the most complex problems at scale. Whether you’re a seasoned data scientist, an operations research specialist, or a budding analytics enthusiast, arenaflex offers a platform where your ideas can shape the next generation of retail experiences. Why This Role Matters In the fast‑moving world of omnichannel commerce, the ability to predict reputed company, optimize inventory, and streamline logistics is the difference between delighting customers and losing market share. The arenaflex reputed company team is at the forefront of this transformation, developing data‑driven solutions that enable seamless product availability, cost‑effective fulfillment, and sustainable growth. As a Senior Data Scientist on this team, you will be instrumental in designing and deploying models that influence millions of transactions every day. Key Responsibilities End‑to‑End Model Development Build, validate, and deploy predictive and optimization models for reputed company forecasting, inventory allocation, and route planning across multiple channels. Advanced Analytics & Machine Learning Apply state‑of‑the‑art techniques—including deep learning, reinforcement learning, and causal inference—to uncover actionable reputed company from large, heterogeneous datasets. Optimization Engine Design Develop linear, mixed‑reputed company, and non‑linear programming formulations to solve complex supply‑chain problems, leveraging solvers such as Gurobi, CPlex, and open‑source alternatives. Data Engineering & Pipeline Management Collaborate with data engineers to design scalable data pipelines using Spark, Hive, and cloud services (GCP, Azure) that feed real‑time analytics workloads. Cross‑Functional Collaboration Partner with product managers, operations leaders, and software engineers to translate business requirements into analytical solutions. Model Governance & Documentation Maintain rigorous documentation, version control, and reproducibility standards for all models and experiments. Thought Leadership & Publication Contribute to internal knowledge bases, present findings at team meetings, and publish research in top-tier conferences and journals. Mentorship & Team Growth Guide junior data scientists and analysts, fostering a culture of continuous learning and innovation. Essential Qualifications Ph.D. or M.S. in Operations Research, Mathematics, Computer Science, Statistics, or a closely related quantitative field. Up to ten years of professional experience in data science, analytics, or operations research. Proficiency in mathematical programming (LP, MILP, NLP) and stochastic modeling. Hands‑on experience with optimization libraries such as Gurobi, CPlex, XPressMP, or open‑source solvers. Strong programming skills in Python, with familiarity in libraries like Scikit‑Learn, TensorFlow, PyTorch, and Keras. SQL expertise for data querying and manipulation. Excellent communication skills, capable of explaining complex technical concepts to both technical and non‑technical stakeholders. Demonstrated ability to work collaboratively in cross‑functional teams. Preferred Attributes Track record of developing end‑to‑end data science solutions that have been deployed in production environments. Experience with cloud platforms (GCP, Azure) and big‑data technologies (Spark, Hive). Published research in peer‑reviewed journals or conferences, particularly in operations research or machine learning. Exposure to supply‑chain domains such as inventory optimization, reputed company planning, or logistics. Knowledge of reinforcement learning, Bayesian inference, or advanced

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