• Architect and ship production-grade LLM applications • Design RAG pipelines that actually work at scale • Collaborate closely with product and engineering to own problems end-to-end • Design, build, and deploy AI models, tools, and agents • Partner with the Head of Data & AI to define the technical architecture • Implement retrieval augmented generation (RAG) pipelines • Design and implement Model Context Protocol (MCP) integrations • Build and maintain MCP servers • Establish patterns for MCP host/client configuration, access control, and observability • Implement MLOps and LLMOps practices • Collaborate with data engineers and platform teams

• Design and implement scalable batch and real-time data processing systems across large and complex datasets. • Build and optimize ETL and streaming data pipelines using modern GCP big data technologies. • Lead development decisions around model choices, data architecture, data modeling, pipeline orchestration, analytics infrastructure, and production systems. • Develop statistical models and analytics capabilities that support product intelligence and operational insights. • Design and maintain production-grade data workflows using technologies such as Airflow, Dataflow, PubSub, and PySpark. • Contribute across multiple areas of the data ecosystem, including data engineering, monitoring and governance, visualization, and analytics tooling. • Establish monitoring, observability, and governance practices for data quality, pipeline reliability, and production health. • Partner closely with Engineering to operationalize scalable data infrastructure and analytics systems. • Collaborate with Product to shape intelligent, data-driven product capabilities and user experiences. • Act as a technical leader and thought partner across data engineering, analytics, infrastructure, and applied modeling initiatives. • Help evolve internal tooling and frameworks that improve scalability, reliability, and operational efficiency across the platform.

Role Description As a Data Scientist at Xplore, you will play a key role in developing and deploying data-driven solutions that support business growth, customer retention, and operational efficiency. You will work on impactful projects such as churn prediction, upsell targeting, and network optimization, applying advanced analytics and machine learning techniques to solve real-world problems. This role is ideal for someone who is passionate about turning data into actionable insights, enjoys hands-on modeling, and wants to grow their expertise in a collaborative environment. - Develop and implement predictive and prescriptive models like: - Customer churn and retention strategies - Upsell and cross-sell opportunity scoring - Product coverage estimation and optimization - Perform feature engineering, model training, validation, performance tuning, and deployment using advanced ML techniques - Collaborate with cross-functional teams (marketing, sales, operations, care, product) to translate business requirements into data science solutions - Support model deployment and monitoring in production environments - Conduct exploratory data analysis and provide insights to inform business decisions - Document methodologies and ensure reproducibility of work - Stay current with emerging tools and techniques in machine learning and analytics Qualifications - 3–5+ years of experience in applied data science or machine learning - Hands-on experience with predictive modeling, time-series forecasting, and statistical analysis - Proficiency in Python and ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch) - Extensive experience with SQL and data manipulation in large datasets - Experience with cloud platforms and distributed computing - Strong problem-solving and analytical skills - Ability to communicate technical concepts to non-technical stakeholders - Familiarity with A/B testing and experimentation frameworks is a plus - Experience with end-to-end project ownership including deployment is an asset - Bachelor’s or Master’s in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Engineering) 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. Details will be supplied to applicants as they move through the selection process. Benefits - 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 looking for a highly skilled Senior AI Data Scientist who thrives at the intersection of analytics, product thinking, and innovation. The ideal candidate will be responsible for developing Data Science & ML algorithms to solve complex, real-world business problems across the team.blue group. This role requires strong analytical skills, programming and data wrangling skills, good communication abilities and up-to-date knowledge of the latest Data Science/ML/AI developments. - Break down multi-domain business problems with a strong focus on return on investment and impact metrics, and think through the appropriate technical methods from POC to production. - Develop solutions and extract insight from data using data science and classical machine learning methods and generative AI (GenAI). - Take ownership of developing, implementing, and automating robust data science and ML models and pipelines, including feature generation, modeling, evaluation, retraining and deployment. - Translate sophisticated analytical findings and model outcomes into clear, actionable insights for stakeholders, demonstrably driving value and improvements in key business metrics. - Collaborate with cross-functional teams to understand requirements and deliver innovative Data Science/ML/AI solutions. - Conduct research on the latest Data Science/ML/AI developments and apply them to improve our systems. - Document processes, code, and findings for future reference and knowledge sharing. Qualifications - 8+ years hands-on industry experience solving data science and machine learning problems to address real-world business problems. - Expert programming skills in Python and SQL and with relevant Data Science & ML frameworks (Scikit-learn, Pandas, PyTorch, etc). - Ability to think through problems from fundamental principles. - Deep understanding of statistical and ML algorithms, methodologies and approaches, and with deep learning & LLMs. - Experience in applying statistical concepts (e.g. regressions, A/B tests, clustering, probability) to business problems. - Expertise at telling stories with data. - Strong problem-solving skills with excellent communication and documentation skills with ability to craft analysis into well-written and persuasive content. - Ability to work collaboratively with cross-functional teams. Requirements - Master's or PhD degree in Computer Science, AI, Machine Learning, or a related field (nice to have). - Software engineering background, ideally in backend or system integration (nice to have). - Deep understanding of current DataOps & MLOps practices, deploying and monitoring models and ETL (extract, transform and load) pipelines (nice to have). - Experience with AI/ML R&D (nice to have). - Experience with distributed data tools (e.g. Spark, Dask, Apache Arrow) (nice to have). Right to Work At any stage, please be prepared to provide proof of eligibility to work in the country you’re applying for. Unfortunately, we are unable to support relocation packages or sponsorship visas. Diversity & Inclusion Everyone is welcome here. Diversity & Inclusion are at our core. Far above any technical competence, we value respect, openness, and trusted collaboration. We do not tolerate intolerance. ESG At team.blue , our commitment to caring for the environment and each other is at the heart of everything we do. Our latest impact report showcases our ongoing ESG efforts and ambitious sustainability goals. Interested in learning more about our dedication to making a positive impact? Check it out here .