This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description As a Lead/Senior Quantitative Analyst, Predictive Modeling, you help shape the future of Phillies Baseball Operations by building statistical models to forecast player performance and communicating those results to decision-makers. Using analytical rigor and sophisticated statistical modeling techniques, you identify opportunities for the Phillies to improve via the application of forecasts to player development and evaluation. Join a team doing cutting-edge foundational research on biomechanics, human movement, ball-flight physics, and more, with the unique opportunity to apply those findings to player evaluation. Responsibilities - Conduct and oversee statistical forecasting projects in multiple baseball subject areas. - Collaborate with baseball subject matter experts in scouting, development, biomechanics, machine learning, decision science, and more, integrating their expertise into player evaluation models. - Maximize organizational impact of the department’s player evaluation models by advocating model-driven decision-making in various baseball contexts. - Ensure projects conform to best practices for implementing, maintaining, and improving predictive models throughout their life cycles. - Assist and mentor other members of the QA team with their projects by providing guidance and feedback on your areas of expertise within baseball and statistical modeling. - Continually enhance your and your colleagues’ knowledge of baseball and data science through documentation, reading, research, and discussion with your teammates and the rest of the front office. Qualifications - 2-5+ years of relevant work or graduate school experience. - Possess or are pursuing a BS, MS or PhD in Statistics or related (e.g., mathematics, physics, or ops research) or equivalent practical experience. - Proficiency with scripting languages such as Python, statistical software (R, S-Plus, SAS, or similar), and databases (SQL). - Demonstrated experience designing, constructing, implementing, and leading technical research projects for use by non-technical stakeholders. - Proven willingness to both teach others and learn new techniques. - Willingness to work as part of a team on complex projects. - Proven leadership and self-direction. Preferred Qualifications - Experience with a probabilistic programming language (Stan, PyMC, etc.). - Experience managing or overseeing the work of other data scientists or analysts. - Experience with model-driven decision-making under uncertainty (e.g., a rigorous approach to fantasy sports, poker, etc.). Application Instructions Interested applicants should submit both their resume and an answer to the following question: The R&D department has been asked to identify the best defensive center fielder in baseball. What models would you build to answer that question, and how would you apply those models to decision-making? (250 word limit) Equal Opportunity Statement We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, age, disability, gender identity, marital or veteran status, or any other protected class.

Role Description Our client's Vision AI platform runs where the data is generated — on-premises, inside government facilities, and at the network edge — not in a hyperscaler cloud. That means the infrastructure has to be bulletproof: - GPU clusters provisioned correctly - Kubernetes workloads scheduled efficiently across heterogeneous compute - Storage performing at the throughput AI training and inference demands - The network capable of handling high-bandwidth, low-latency sensor data at scale As a MLOps / AI Infrastructure Engineer, you will own all of it. You will: - Rack, configure, and operate the on-premises compute and GPU infrastructure that powers the platform - Build and maintain the Kubernetes clusters that orchestrate AI workloads - Design the networking fabric that ties edge nodes to core compute - Implement the MLOps pipelines that take models from development to production You will work directly with our AI/ML engineers, the Lead Architect, and on-site client technical teams to ensure the platform runs reliably in environments that are often air-gapped, physically secured, and subject to strict government compliance requirements. Qualifications - 6+ years of infrastructure engineering experience, with at least 3 years managing GPU compute clusters or HPC environments in production - Deep hands-on expertise with NVIDIA GPU infrastructure: driver lifecycle management, CUDA, DCGM, MIG, NVLink topologies, and the NVIDIA GPU Operator for Kubernetes - Production-level Kubernetes administration experience on bare-metal: cluster provisioning, upgrades, CNI/CSI configuration, RBAC, and day-2 operations - Strong networking fundamentals: BGP, VLAN segmentation, RDMA/RoCE or InfiniBand configuration, load balancing, and firewall policy management - Hands-on experience with software-defined storage (Ceph, Rook-Ceph, or MinIO) in AI/HPC workload contexts — performance tuning, capacity planning, and failure recovery - Practical MLOps experience: model serving infrastructure (Triton or equivalent), experiment tracking (MLflow or Kubeflow), and GitOps-based model deployment pipelines - Working knowledge of NIST SP 800-171 controls and the ability to translate them into concrete infrastructure configurations and audit evidence - Proficiency with infrastructure-as-code tooling: Terraform or Ansible for reproducible, auditable infrastructure builds - Strong Linux systems administration skills (RHEL/Rocky Linux or Ubuntu) including kernel tuning, storage I/O optimization, and systemd service management - Excellent written communication for producing infrastructure runbooks, network diagrams, and compliance documentation in a remote-first environment Requirements - Deploy, configure, and maintain on-premises GPU servers — primarily NVIDIA H200 and A100 nodes - Implement and tune NVIDIA-specific tooling: DCGM, MIG, and NVIDIA Container Toolkit - Manage bare-metal provisioning workflows to enable repeatable, auditable server builds - Monitor hardware health, capacity utilization, and thermal/power envelopes - Build, upgrade, and maintain production-grade Kubernetes clusters - Design and operate cluster networking using CNI plugins - Configure and manage MetalLB or equivalent load balancing and service mesh components - Implement resource quotas, LimitRanges, and node affinity/taints - Maintain cluster security posture: RBAC policies, Pod Security Admission, and network policies - Deploy and operate MLOps platforms for experiment tracking and pipeline orchestration - Configure and manage NVIDIA Triton Inference Server - Build CI/CD pipelines for model deployment - Optimize GPU utilization for batch training jobs and latency-sensitive inference services - Manage model artifact storage and versioning using software-defined storage backends - Design and implement the high-bandwidth network fabric required for GPU cluster interconnects - Deploy and operate software-defined storage solutions - Configure network segmentation, VLANs, and firewall policies - Establish and maintain VPN or secure tunneling solutions - Implement infrastructure controls mapped to NIST SP 800-171 and CMMC requirements - Maintain hardened OS baselines across all infrastructure nodes - Produce and maintain infrastructure documentation required for government procurement - Support penetration testing engagements Benefits - Hands-on ownership of demanding AI infrastructure in the public sector - A technically rigorous environment where your infrastructure decisions affect mission-critical operations - Competitive, globally benchmarked compensation including base salary, equity, and performance bonus - Fully remote with async-first culture; periodic travel for deployments and planning - Access to cutting-edge NVIDIA hardware and budget for relevant certifications - Collaboration with a Lead Architect and engineering team who understand infrastructure as a product

Ciklum is looking for a Senior Data Scientist to join our team full-time in the US . We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live About the role: As a Senior Data Scientist, become a part of a cross-functional development team engineering experiences of tomorrow. Client is building an Agentic AI health platform to focus on risk stratification for chronic diseases by creating a value based PMPM model to increase longevity by attacking these conditions. As their main goal is to focus on lifestyle based pathways, they are starting with weight loss management and diabetes. Need is to establish a team who can work with in US and come to customer meetings with him to lay out Data analytics, population health risk stratification and other thoughts. Responsibilities: - Sr Health Informatics/Data Scientists: deeply technical in statistical analysis, data science and ML algorithms; hands-on experience with Python to build data models specific to Risk Stratification and RISK TIER migration in US healthcare; familiar with reimbursement codes for Medicare, Medicaid, etc - Health informatics leader - data analytics, architect, pop health, risk stratification, risk modeling - part time is ok if full time is not available - this will be the key person here for him. he/she needs to be. in customer meetings with him - Collaborate with engineers, data scientists, and BA to understand requirements, refine models, and integrate LLMs into AI solutions - Embed generative AI solutions into consolidation, reconciliation, and reporting processes - Dev and implementation of Deep learning algorithms for AI solutions - Stay updated with recent trends in GENAI and apply the latest research and techniques - Preprocess raw data, including text normalization, tokenization, and other techniques, to make it suitable for use with NLP models - Setup and train LLMs and other state-of-the-art neural networks - Conduct thorough testing and validation to ensure accuracy and reliability of model implementations - Perform statistical analysis of results and optimize model performance for various computational environments, including cloud and edge computing platforms - Perform model audits to identify and mitigate risks - Monitor and optimize generative models for performance and scalability Requirements: - Solid understanding of object-oriented design patterns, concurrency/multithreading, and scalable AI and GenAI model deployment - Strong programming skills in Python, PyTorch, TensorFlow, and related libraries - Proficiency in RegEx, Spacy, NLTK, and NLP techniques for text representation and semantic extraction - Hands-on experience in developing, training, and fine-tuning LLMs and AI models - Practical understanding and experience in implementing techniques like CNN, RNN, GANs, RAG, Langchain, and Transformers - Expertise in Prompt Engineering techniques and various vector databases - Familiarity with Azure Cloud Computing Platform - Experience with Docker, Kubernetes, CI/CD pipelines - Experience with Deep learning(*), Computer Vision(*), CNN, RNN, LSTM - Experience with Vector Databases (Milvus, Postgres, etc.)(*), Database Technologies What`s in it for you? - Strong community: Work alongside top professionals in a friendly, open-door environment - Growth focus: Take on large-scale projects with a global impact and expand your expertise - Tailored learning: Boost your skills with internal events (meetups, conferences, workshops), Udemy access, language courses, and company-paid certifications - Endless opportunities: Explore diverse domains through internal mobility, finding the best fit to gain hands-on experience with cutting-edge technologies - Care: Healthcare, Basic Life Insurance, Short and Long-term disability insurance according to the Company’s Benefit Plans About us: At Ciklum, we are always exploring innovations, empowering each other to achieve more, and engineering solutions that matter. With us, you’ll work with cutting-edge technologies, contribute to impactful projects, and be part of a One Team culture that values collaboration and progress. In the US, Ciklum is growing fast—inviting experienced professionals to lead digital transformation alongside Fortune 500 clients. Be part of a company where innovation and impact go hand in hand. Want to learn more about us? Follow us on Instagram, Facebook, LinkedIn. Explore, empower, engineer with Ciklum! Interested already? We would love to get to know you! Submit your application. We can’t wait to see you at Ciklum.

• Analyzes business requirements and determines a suitable solution autonomously, evaluating if an ML-based solution is feasible • Good understanding of business requirements • Develops and fine-tunes models through reproducible experiments • Builds ML solutions incorporating software engineering quality standards (SDLC) and data engineering best practices • Participates in the technical design of features with guidance • Understands and optimizes and monitors model performances • Prioritizes tasks with autonomy based on requirements and proper context