• 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.

Role Description We are seeking a highly skilled and technically proficient Director of Data Science to lead and scale our data science team. This individual will be responsible for driving complex modeling efforts, designing robust data pipelines, and delivering high-impact analytical solutions that influence business strategy. This role is ideal for a hands-on leader with deep expertise in machine learning, statistics, and data infrastructure. Responsibilities - Team Leadership & Strategy - Lead and mentor a multidisciplinary team of data scientists and ML engineers. - Partner cross-functionally with engineering, product, analytics, and business stakeholders to identify opportunities for data-driven innovation within an agile environment. - Uncover deep insights from data and effectively connect them to user behaviors through compelling storytelling. - Technical Execution - Architect, deploy, and validate advanced machine learning models and algorithms for prediction, optimization, segmentation, and recommendation. - Review and guide experimentation, statistical modeling, and A/B testing across products and initiatives. - Ensure scalable deployment of ML solutions into production in collaboration with engineering teams. - Operational Excellence - Define and uphold best practices in model development, versioning, monitoring, and reproducibility. - Implement frameworks for model governance and performance tracking. - Contribute to building a modern data science stack that supports experimentation, feature engineering, and model lifecycle management. - Data Infrastructure & Collaboration - Guide the development of robust data pipelines, feature stores, and model-serving architectures. - Advocate for sound data management and partner with data engineering on upstream data quality and infrastructure needs. - Thought Leadership - Stay abreast of emerging techniques in AI/ML and evaluate applicability to our business challenges. - Cultivate a culture of curiosity, experimentation, and scientific rigor. Qualifications - 8+ years in data science or machine learning roles, with at least 3–5 years leading teams. - Degree in Computer Science, Statistics, Mathematics, or a related technical field (MS/PhD preferred, or undergraduate degree with relevant work experience). - Deep experience with Python, SQL, and modern ML libraries (e.g., scikit-learn, statsmodels, TensorFlow, PyTorch). - Proven ability to scale ML solutions in production environments (e.g., via cloud platforms like AWS/GCP). - Experience in digital product development, particularly with web or app platforms, demonstrating an ability to effectively contribute to the lifecycle of digital products. - Strong understanding of experimental design, causal inference, and statistical testing. - Exceptional communication skills with the ability to translate technical work into business impact. Benefits - Paid Family Care for parents and caregivers for 12 weeks or more. - Monetary assistance and support for Adoption, Surrogacy, and Fertility. - Monetary assistance and support for pet adoption. - Employee Assistance Programs and Health/Wellness/Comfort reimbursements to help you invest in your future and work/life balance. - Tuition Assistance. - Paid time off that includes Flexible Time off, Vacation, Annual Sick Days, Volunteer Days, Holiday and Identity days, and more. - Matching Gifts programs. - Flexible working arrangements. - ‘Work Your World’ Program encouraging employees to work from anywhere. - Publicis Groupe has an office for up to 6 weeks a year (based upon eligibility). - Business Resource Groups that support multiple affinities and alliances.