Role Description Join us in building the intelligence that powers product discovery for millions of shoppers and thousands of merchants across the Middle East. As a Senior Data Scientist for the Recommendation Systems Pod, you'll lead the design and execution of large-scale personalization models that directly impact the company topline. This is a rare opportunity to shape the next generation of commerce AI in a high-growth market characterized by highly diverse user and merchant behaviors across the GCC. - Design, train, and deploy recommendations/personalization models leveraging deep learning, sequence models (Transformers, GRU), and boosted trees (XGBoost, LightGBM). - Develop multi-objective ranking that blends engagement, conversion, and merchant value into a single ranking score (value model), using multi-task learning where shared representations help. - Build scalable two-stage retrieval and ranking systems — ANN retrieval (FAISS, ScaNN) over user/product/event embeddings feeding learning-to-rank models (pointwise, pairwise, and listwise objectives). - Collaborate with infra to productionize real-time feature pipelines (ClickHouse, Kafka, Spark). - Define serving-time impression and feature logging to eliminate training-serving skew and produce unbiased training data. - Design and run online experiments with rigorous guardrail metrics; correct for position and presentation bias in logged data; apply counterfactual/off-policy evaluation and uplift modeling to attribute lift accurately. - Integrate model outputs with platform APIs for dynamic personalization in search, home feeds, and store pages. - Define best practices for offline evaluation (MAP@K, NDCG) and online experimentation metrics (CTR, CVR, GMV uplift). - Partner with product analytics and data science to iterate on signal enrichment and cold-start strategies. - Mentor junior data scientists and define best practices. Qualifications - Bachelor's or Master's degree in Computer Science, Machine Learning, or a related technical field. - 4+ years of hands-on ML experience, including 2+ years designing or deploying large-scale recommendation systems. - Track record: Built or maintained systems serving 1M+ users or generating 100M+ personalized predictions daily. - Deep expertise in representation learning, embeddings, attention mechanisms, and multi-task learning. - Demonstrated success integrating multi-stage ranking systems across e-commerce surfaces (search, feeds, product detail pages) with measurable online lift (CVR, GMV). - Proficient with large-scale data ecosystems: Kafka, Spark, ClickHouse, BigQuery, or equivalent. - Strong command of experimentation rigor: guardrail metrics, position-bias correction, off-policy/counterfactual evaluation, and model monitoring. - Skilled in debugging, optimization, and productionization of ML pipelines in cloud or containerized environments.

• Develop analyses to identify patterns, trends, and correlations among indicators • Implement mechanisms for automated generation of insights for governance • Explore the use of AI / LLM models for interpreting and explaining data • Develop a conversational assistant solution for querying and analyzing information • Support the evolution of the governance model toward a data-driven approach

Title: High Explosive Modeler - Staff Scientist Location: Livermore United States Employees work in a hybrid modemay offer a hybrid schedule Full-time Organization: Physical and Life Sciences Category: Physical Life Sciences Job Code 2: SES.4 Science & Engineering MTS 4 Employee Referral Bonus: Not applicable Job Code 1: SES.3 Science & Engineering MTS 3 Pre-Employment Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana) Pre-Placement Medical Exam: Not applicable Security Clearance: Anticipated DOE Q clearance (requires U.S. citizenship and a federal background investigation) Position Type: Flexible Term Job Description: Join us and make YOUR mark on the World! Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability. Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact. We have an opening for a Staff Scientist to perform research, development, and application activities in support of Laboratory missions. You will be an integral member of an interdisciplinary team responsible for conducting and supporting research in the development and application of high explosive models predicting detonation performance in response to complex stimuli. This position is in the Reaction Dynamics Group within the Energetic Materials & Design (EMD) section of the Materials Science Division supporting the Energetic Materials Center (EMC). This position may offer a hybrid schedule, which includes the flexibility to work from home one or more days per week, after a probationary period. The specifics of the hybrid schedule, including the exact number of days required in the office and virtual work options may vary based on the needs of the team and the organization. This position will be filled at either level based on knowledge and related experience as assessed by the hiring team. Additional job responsibilities (outlined below) will be assigned if hired at the higher level. You will - Develop advanced high explosive detonation models based on fundamental chemical and physical mechanisms. - Interact with experimental groups and incorporate information from advanced experiments into current and developmental high explosives models to improve model fidelity. - Systematically validate models against a suite of experimental data. - Develop uncertainty quantification methods based on utilizing statistical approaches and/or machine learning. - Provide solutions and creative approaches to ensure project needs are addressed in a timely and efficient manner. - Contribute to project/program milestone completion and provide solutions to complex problems that may not be well defined. You will be expected to interact closely with other team members as well as work independently with minimal direction when appropriate. - Interact with the physics and engineering design staff and other users to support responsive model development and insertion options. - Perform other duties as assigned. Additional job responsibilities, at the SES.4 level - Contribute to developing a strategy to improve detonation models by incorporating initial conditions, morphology, and chemical variations to expand capabilities into adjacent domains. - Actively support and guide experimental campaigns involving energetic materials to benchmark current models and advance model development to address complex problems relevant to national security missions. - Create innovative methods, technologies, and knowledge in detonation modeling. - Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. Citizenship. - PhD. in chemistry, physics, or the equivalent combination of education and related experience. - Knowledge of shock wave physics, physical and chemical kinetics, statistical and thermodynamic methods. - Familiarity with scientific programming, code development practices, and proficiency in one or more high-level programming/scripting languages. - Advanced written and verbal communication skills necessary to deliver presentations and prepare written reports, explain technical information. - Advanced interpersonal skills necessary to interact with a diverse set of scientists, engineers, and other technical and administrative staff. Additional qualifications at the SES.4 level - Demonstrated expertise in detonation physics and energetic materials modeling. - Demonstrated ability to lead complex technical projects and a strong track record mentoring scientific staff. - Strong track record of publications and/or technical reporting. Qualifications We Desire - Experience working in energetic materials research at a Department of Energy (DOE) and/or Department of Defense (DoD) facility or equivalent with stringent safety and security policies. - Knowledge of supercomputer applications in material modeling and energetic materials research. - Knowledge of machine learning techniques applicable to material model development and/or complex hydrodynamic simulations. Pay Range $175,530 - $267,060 Annually $175,530 - $222,564 Annually for the SES.3 job level $210,630 - $267,060 Annually for the SES.4 job level This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting; pay will not be below any applicable local minimum wage. An employee's position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs. #LI-Hybrid Position Information This is a Flexible Term appointment, which is for a definite period not to exceed six years. If final candidate is a Career Indefinite employee, Career Indefinite status may be maintained (should funding allow). Why Lawrence Livermore National Laboratory? - Included in 2026 Best Places to Work by Glassdoor! - Flexible Benefits Package - 401(k) - Relocation Assistance - Education Reimbursement Program - Flexible schedules (*depending on project needs) Security Clearance This position requires a Department of Energy (DOE) Q-level clearance. If you are selected, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing. Q-level clearance requires U.S. citizenship. Pre-Employment Drug Test External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor. Wireless and Medical Devices Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices. If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings. Equal Employment Opportunity We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.

Title: Senior Data Scientist, Inventory 360 (Operations Research) Location: United States Remote time type Full time job requisition id 202605146 Job Description: At DICK'S Sporting Goods, we believe in how positively sports can change lives. On our team, everyone plays a critical role in creating confidence and excitement by personally equipping all athletes to achieve their dreams. We are committed to creating an inclusive and diverse workforce, reflecting the communities we serve. If you are ready to make a difference as part of the world's greatest sports team, apply to join our team today! OVERVIEW: Are you a passionate technologist with experience in AI, Machine Learning, Operations Research and Data Science? Are you looking for an opportunity to drive enterprise impact and shape the future of a leading sports retailer with $12B+ in revenue and 800+ physical stores? Do you enjoy working with a highly skilled team of Machine Learning engineers & Scientists, co-creating enterprise grade AI capabilities? We are seeking a Senior Data Scientist - Inventory 360 to lead the development of intelligent decisioning systems that optimize inventory flow and operations and drive exceptional customer experiences. This role sits at the intersection of machine learning, operations research, and enterprise systems - powering decisions that determine how, when, and where we fulfill customer demand. This role requires a subject matter expert with deep experience in traditional machine learning and cutting edge AI with a strong foundation in operations research. You'll apply advanced modeling techniques to design intelligent decisioning systems that optimize fulfillment operations and elevate the customer experience. Your work will focus on solving complex foreacasting and optimization problems - from assortment planning to purchase order optimization to allocation -using a blend of predictive analytics, simulation, and mathematical programming. JOB PURPOSE As a senior data scientist, you'll influence the enterprise decisioning landscape by developing models that integrate with high-impact systems across merchandising and inventory planning and pricing optimization. You'll collaborate with product, engineering, and business leaders to translate operational challenges into solvable data science problems, and help them understand the art of the possible through rigorous experimentation, simulation, and model design. Your work will directly influence delivery speed, order accuracy, and service reliability - ensuring every customer interaction is fast, efficient, and frictionless. RESPONSIBILITIES - Develop ML and OR-based models for demand forecasting, assortment planning, purchase order optimization, inventory allocation and price optimization. - Apply techniques such as mixed-integer programming, dynamic programming, graph theory, spatial optimization and simulation to solve real-time decisioning problems. - Integrate predictive ML models with optimization logic to enable adaptive, data-driven decisions. - Build and operationalize decision engines that automate fulfillment decisions across the enterprise. - Collaborate with engineering to deploy models into production systems with real-time data pipelines and monitoring. - Ensure models are interpretable, auditable, and aligned with business constraints. - Combine ML outputs with OR solvers via hybrid decision frameworks, enabling scenario-aware optimization and policy simulation. - Ensure robustness and scalability of models by leveraging containerized environments and observability tools - Enable real time decisioning by building & incorporating streaming pipelines and supporting low latency inference and optimization. - Partner with product and operations to define decision boundaries, constraints, and success metrics. - Communicate insights and model performance to technical and nontechnical audiences. - Understand latest research in the field of OR and AI to give inputs to enterprise roadmaps to ensure we are on the path to build Best in Class merchandising planning and optimization solution PREFERRED SKILLSET - Advanced degree (MS/PhD) in Operations Research, Computer Science, Statistics, or related field. - 4+ years of experience in building optimization and ML models in assortment planning, optimization, fulfillment or supply chain domains. - OR Techniques: linear/mixed-integer programming, simulation, queuing theory. - ML Tools: Python, PyTorch/TensorFlow, scikit-learn. - Data & Infra: SQL, Spark, Airflow, cloud platforms (Azure, AWS, GCP). - Solid understanding of distributed systems, APIs, and cloud infrastructure (Azure, AWS, or GCP). - Familiarity with reinforcement learning or contextual bandits for adaptive decisioning in dynamic environments. - Familiarity with graph algorithms and path planning for spatial routing and pick path optimization. - Skilled in designing and analyzing A/B tests or switchback experiments for operational models. - Experience in an Agile working environment and at least one related project management tool (Azure DevOps, Jira, etc.) - Comfortable presenting results to cross functional partners and help them understand technical trade offs - Brings a collaborative, problem solving and growth mindset to all interactions with a strong focus on delivery. - Experience with real-time decisioning systems and streaming data architectures. - Familiarity with reinforcement learning or hybrid ML-OR frameworks. - Background in eCommerce, retail, or customer-facing fulfillment systems. - Strong understanding of experimentation design and causal inference. QUALIFICATIONS: - Education: Bachelor's Degree or equivalent level preferred - General Experience: Experience enables job holder to deal with the majority of situations and to advise others (Over 3 years to 6 years) - Managerial Experience: Basic experience of coordinating the work of others (4 to 6 months) VIRTUAL REQUIREMENTS: At DICK'S, we thrive on innovation and authenticity. That said, to protect the integrity and security of our hiring process, we ask that candidates do not use AI tools (like ChatGPT or others) during interviews or assessments. To ensure a smooth and secure experience, please note the following: - Cameras must be on during all virtual interviews. - AI tools are not permitted to be used by the candidate during any part of the interview process. - Offers are contingent upon a satisfactory background check which may include ID verification. If you have any questions or need accommodations, we're here to help. Thanks for helping us keep the process fair and secure for everyone! Targeted Pay Range: $83,000.00 - $138,200.00. This is part of a competitive total rewards package that could include other components such as: incentive, equity and benefits. Individual pay is determined by a number of factors including experience, location, internal pay equity, and other relevant business considerations. We review all teammate pay regularly to ensure competitive and equitable pay.DICK''S Sporting Goods complies with all state paid leave requirements.