Data Scientist

Job summary

Lead client discovery and develop, evaluate, and operationalize predictive, generative, and agentic AI solutions using Azure Databricks and Microsoft technologies. Implement governed MLOps and LLMOps practices, communicate results to stakeholders, and drive adoption and measurable business outcomes.

Job details

At Lantern, our company culture stands as the bedrock of our success and a source of pride for our teams. We firmly believe that a culture founded on trust forms the basis for enduring relationships with clients, colleagues, and partners. Within this culture, we nurture an environment of respect, inclusion, and belonging, fostering collaboration among inspired teams. We prioritize the well-being of our colleagues, the success of our clients, and our positive impact on society. Embracing a growth mindset where curiosity thrives, we celebrate excellence and value individuals who inspire and mentor others, elevating the collective. Our driving force lies in personal and business growth. We go above and beyond to surprise and delight our clients, delivering tangible business value. In facing challenges, we make tough choices and solve complex problems to positively influence our clients, their customers, and the world at large. As a Microsoft services partner, we hold ourselves to the highest standards of technical excellence. This commitment to quality is evident not only in our work but also in how we support and empower our employees. At Lantern, our culture mirrors our core values and unwavering dedication to realizing our purpose and vision, making it a dynamic and fulfilling workplace. Together, we transcend the ordinary and achieve extraordinary results. Lantern is seeking a hands-on, client-facing Senior Data Scientist to design, develop, and operationalize advanced analytics, machine learning, generative AI, and agentic AI solutions using Azure Databricks and the Microsoft data and AI ecosystem. You will partner with client stakeholders, data engineers, architects, and application teams to translate business problems into secure, governed, scalable, and explainable AI solutions - from experimentation and feature engineering through deployment, monitoring, and measurable adoption. Key Responsibilities Lead discovery sessions to define analytical problems, success metrics, data requirements, experimentation plans, and production roadmaps. Explore and prepare structured and unstructured data; perform feature engineering, statistical analysis, and model selection using Python, SQL, Spark, and Databricks notebooks. Build, evaluate, and tune predictive, forecasting, optimization, natural language processing, computer vision, generative AI, and agentic AI solutions. Use Azure Databricks capabilities such as Delta Lake, Unity Catalog, MLflow, Feature Engineering, Model Serving, Mosaic AI, and vector search to create governed, production-ready solutions. Integrate solutions with Microsoft Azure AI Foundry, Azure OpenAI, Microsoft Fabric, OneLake, Power BI, and Azure services as appropriate. Implement MLOps and LLMOps practices including source control, automated testing, CI/CD, model registration, deployment, monitoring, drift detection, responsible AI, and cost optimization. Communicate findings and model behavior through clear visualizations, executive-ready narratives, demonstrations, and technical documentation. Own assigned workstreams, manage risks and dependencies, and collaborate with client teams to drive adoption and measurable business outcomes. Contribute to proposals, reusable accelerators, technical standards, peer reviews, mentoring, and the growth of Lantern’s Databricks and Microsoft AI practices. Skills, Knowledge and Expertise 5+ years of experience developing and delivering data science, machine learning, or AI solutions, preferably in consulting or other client-facing environments. Strong hands-on experience with Azure Databricks for data preparation, distributed model development, experiment tracking, model lifecycle management, and production deployment. Advanced proficiency in Python and SQL, with practical experience in PySpark and common data science frameworks such as scikit-learn, XGBoost, TensorFlow, or PyTorch. Strong foundation in statistics, supervised and unsupervised learning, feature engineering, model evaluation, explainability, and experimental design. Experience developing generative AI solutions using large language models, retrieval-augmented generation, embeddings, vector databases, prompt engineering, evaluation, and agentic patterns. Working knowledge of Microsoft Azure AI Foundry, Azure OpenAI, Microsoft Fabric, OneLake, Power BI, and the broader Azure data and AI ecosystem. Experience with MLOps or LLMOps, CI/CD, model serving and monitoring, data governance, Unity Catalog, security, responsible AI, performance, and cost optimization. Strong consulting, communication, visualization, documentation, problem-solving, and stakeholder-management skills, with the ability to explain complex models to technical and executive audiences. Preferred Certifications And Credentials Databricks Certified Machine Learning Professional or Machine Learning Associate Databricks Certified Generative AI Engineer Associate Databricks Certified Data Engineer Associate or Professional Microsoft Certified: Azure Data Scientist Associate (DP-100) Microsoft Certified: Azure AI Engineer Associate (AI-102) Microsoft Certified: Fabric Data Engineer Associate (DP-700) or Fabric Analytics Engineer Associate (DP-600) Databricks Solutions Architect Champion is highly preferred and considered a strong differentiator

What you’ll do

Lead client discovery and develop, evaluate, and operationalize predictive, generative, and agentic AI solutions using Azure Databricks and Microsoft technologies. Implement governed MLOps and LLMOps practices, communicate results to stakeholders, and drive adoption and measurable business outcomes.

Requirements

Requires 5+ years of experience delivering data science, machine learning, or AI solutions, strong Python, SQL, PySpark, and Azure Databricks skills, and a solid foundation in statistics and model development. Candidates should also have experience with generative AI, production operations and governance, and strong consulting and communication skills; relevant Databricks and Microsoft certifications are preferred.

Listed skills

• SQL · Preferred
• Machine learning · Preferred
• Stakeholder Management · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Azure Databricks
• Python
• SQL
• PySpark
• Machine Learning
• Generative AI
• Agentic AI
• MLOps
• LLMOps
• Feature Engineering
• Statistical Analysis
• Model Evaluation
• Retrieval-Augmented Generation
• Azure OpenAI
• Microsoft Fabric
• Stakeholder Management

Job areas

• Data & Analytics
• Technology
• Science & Research
• Consulting
• Software