• Research, design, and build ML models across a range of use cases • Work across the full deployment spectrum: managed cloud ML platforms and containerized solutions built in Python • Collaborate with engineers to integrate models into production environments and uphold MLOps best practices • Preprocess and analyze large datasets; think critically about data quality and what it means for model reliability • Contribute to technical discussions across the team, fostering a culture of shared learning and continuous improvement • Translate complex ML concepts clearly for both technical collaborators and non-technical client stakeholders

• Own client engagements end-to-end: scope the problem, build the solution, manage expectations, and deliver • Lead small project teams and serve as the technical and strategic point of contact for clients • Design, build, and deploy ML solutions across a range of domains and use cases in a landscape where no two projects are the same • Work across the full deployment spectrum including managed cloud ML platforms (SageMaker, Vertex AI, Azure ML) and containerized Python solutions running on raw infrastructure • Mentor junior and senior data scientists and help shape how we practice ML at OneSix • Evaluate emerging tools and techniques and bring the right ones to bear on client problems

Role Description This Data Scientist role will be a part of the Decision Sciences team in Client Center of Excellence organization. The role will be responsible for interpretation of data and business requirements to develop customer centric data driven insights and AI/ML solutions. Working in collaboration with Director, Decision Sciences and other data scientists team members, this role works to: - Assist in development of advanced AI/ML systems using techniques such as deep learning, representation learning, time-series modeling, survival analysis, and probabilistic modeling to solve complex healthcare problems. - Develop Generative AI and LLM-powered solutions including retrieval-augmented generation (RAG) pipelines, domain-adapted LLMs, and AI copilots for enterprise workflows. - Work with business stakeholders to develop business solutions based on data driven insights to solve complex business problems. - Be a key contributor in projects that combine diverse customer data sets to identify trends and opportunities, develop custom machine learning models, and provide recommendations to optimize the business and address data-related problems. - Coordinate with different functional teams to improve customer data architecture, ensure accuracy of model output and implement new data gathering processes. - Create customized summaries, business presentations and documentation for internal business clients based on the results of research. - Collaborate with engineering, platform, and product teams to operationalize scalable AI/ML and GenAI systems within enterprise platforms. Qualifications - High School Diploma/GED - 2+ years of experience providing data driven solutions, reports, analytics or recommendations using SQL and/or data visualization tools like PowerBI, Tableau or equivalent. - 1+ years of experience in leveraging machine learning, artificial intelligence, or applied data science to help answer business questions. - 1+ years of experience in Python-based machine learning development using frameworks such as PyTorch, TensorFlow, or equivalent. - 1+ years of experience designing and deploying production reporting, dashboards, ML models or supporting production ETL jobs. - 1+ years of experience of developing machine learning systems leveraging regression, natural language processing algorithms, classification and clustering, time-series forecasting and predictive/prescriptive modeling. Requirements - Experience working with distributed ML systems and large-scale data platforms such as Snowflake, Spark, Databricks, or other ML systems. - Experience working on cloud platforms such as AWS, Azure, or GCP. - Experience working with healthcare datasets and standards such as claims, EHR, ICD, CPT, SNOMED, FHIR, or HL7. - Experience working on development and support of data products in a business environment. Benefits - Comprehensive benefits package. - Incentive and recognition programs. - Equity stock purchase. - 401k contribution (all benefits are subject to eligibility requirements).

Role Description As a Senior Data Scientist, you will develop and deploy the predictive analytics and ML models that enable Xplore's network to become self-monitoring and increasingly self-optimizing across Fiber, Fixed Wireless, and Satellite platforms. - Translate high-resolution network telemetry, operational event data, and customer experience signals into production ML models that reduce incident response times, improve capacity planning accuracy, and surface network degradation before customers are impacted. - Build and operate solutions across both cloud and on-premises environments, supporting migration initiatives and designing, developing, and deploying capabilities that run natively on-prem where needed. - All modeling work is built and served on Databricks, leveraging Gold-tier data assets and integrating with automation and observability pipelines operated by the broader network intelligence squad. - Mentor data scientists on the team and serve as the technical ML authority for the Network Intelligence domain. Key responsibilities include: - Develop and deploy ML models for anomaly detection, predictive failure, capacity forecasting, and network performance degradation using Databricks and MLflow. - Build feature engineering pipelines from network telemetry (RAN KPIs, alarms, traffic counters, customer experience data) using PySpark and Python against Gold-tier Databricks assets. - Design and maintain MLflow experiment tracking, model registries, and retraining pipelines to support production model lifecycle management. - Evaluate and apply appropriate algorithms matched to network time-series and event-driven data patterns: gradient boosting, LSTMs, isolation forest, survival models, and similar. - Build optimization and scoring frameworks to prioritize network interventions: incident severity scoring, site risk tiering, maintenance urgency ranking. - Integrate model outputs with automation pipelines so that insights translate into actionable triggers for network operations teams. - Translate complex model outputs into clear narratives, visualizations, and Databricks AI/BI dashboard inputs consumable by engineering, operations, and executive audiences. - Partner with Data Engineers to specify feature datasets, ensure data quality, and align on pipeline dependencies. - Present findings and model performance to Director and VP-level stakeholders with confidence and clarity. Qualifications - 7-10+ years of applied data science or ML engineering experience in a production environment. - Strong proficiency in Python for data science: pandas, scikit-learn, XGBoost, and PyTorch or TensorFlow. - Hands-on experience building and deploying data science or ML solutions across both cloud and on-premises environments, including Databricks for model development, MLflow for experiment tracking, and Delta Lake for feature management. - Solid foundation in statistical modeling, time-series analysis, and anomaly detection methods. - Experience working with large-scale, high-frequency telemetry or event-driven datasets. - Familiarity with SHAP or other model explainability frameworks for stakeholder communication. - Ability to frame business problems as ML problems and communicate results clearly to non-technical audiences. - Strong communication skills and demonstrated mentorship experience. Preferred Qualifications - Background in network operations, telecommunications, or infrastructure analytics. - Familiarity with network performance metrics: throughput, latency, packet loss, RSRP/RSRQ, SINR, or equivalent RAN KPIs. - Exposure to reinforcement learning or simulation-based optimization for network decisioning. - Experience with Databricks Feature Store or equivalent feature platform tooling. - Databricks Certified Machine Learning Professional. - Bachelor's or Master's degree in Computer Science, Statistics, Applied Mathematics, or a related field. Requirements - As a condition of employment and in order to comply with industry related data security standards, this position is subject to the successful completion of a Criminal Background Check. - Xplore is committed to creating an accessible environment and will accommodate disabilities during the selection process. Please let your recruiter know during the selection process of any accommodation needs.