Jobs at MUTT DATA

Design and analyze experiments, apply causal inference, and measure marketing incrementality, attribution, ROI, CLV, and marketing mix performance. Build predictive and segmentation models for growth, pricing, promotions, retention, and personalization. Use Databricks, Spark, Python, and SQL to develop scalable analyses and collaborate with engineering teams. Communicate rigorous findings and recommendations to marketing, commercial, and business stakeholders.

Design and analyze experiments, apply causal inference methods, measure marketing incrementality and ROI, and build predictive and segmentation models. Translate growth and marketing questions into rigorous analytical solutions using Databricks, Python, SQL, and statistical techniques. Collaborate with engineering and commercial stakeholders to support acquisition, retention, pricing, promotions, and customer analytics decisions.

Build and maintain Power BI dashboards, reports, data models, datasets, and semantic layers. Develop reliable data transformations and production pipelines using Databricks, Microsoft Fabric, PySpark, Delta Lake, and SQL. Collaborate with stakeholders to define KPIs, monitor data quality, troubleshoot reporting issues, and document reporting processes.

Develop machine learning and AI solutions for recommendation, personalization, segmentation, behavioral analysis, churn prediction, and notification optimization. Perform exploratory and statistical analysis, feature engineering, model evaluation, and scalable implementation using large datasets. Collaborate with Product, Engineering, and Business teams to translate challenges into data products and communicate actionable insights to technical and non-technical stakeholders.

Designs and optimizes pricing and Revenue Growth Management models using machine learning, statistics, econometrics, forecasting, elasticity analysis, and mathematical optimization. Builds predictive models, experiments, simulations, and scenario analyses using commercial data. Validates model robustness and stability, collaborates with business, data engineering, and ML teams, and communicates actionable insights to technical and non-technical stakeholders.

Develop advanced pricing and revenue growth models using machine learning, statistics, econometrics, forecasting, and optimization. Build price elasticity and demand models, conduct experiments and validation, perform scenario simulations, and translate commercial data into actionable recommendations. Collaborate with business stakeholders, data engineers, and machine learning engineers while ensuring model robustness, stability, and scalability using Databricks, Spark, and MLflow.

Designs, implements, and operates MarTech architectures spanning CDPs, reverse ETL, identity resolution, consent, activation platforms, and customer data foundations. Integrates marketing platforms with Databricks and cloud data systems, operationalizes AI-driven audiences and journeys, and establishes production governance, monitoring, and data quality. Advises clients on technology trade-offs, supports discovery and proposals, and develops reusable consulting offerings.

Lead client-facing AI and machine learning implementations from architecture through production deployment. Build scalable ML pipelines, MLOps workflows, Generative AI and agentic applications, RAG systems, and distributed data solutions using Python, Docker, Databricks, and Apache Spark. Translate business needs into technical solutions, manage infrastructure, communicate with stakeholders, support consulting practice growth, and document client engagements.

Process invoices and expenses, maintain accounting records, prepare accruals and provisions, perform bank and credit card reconciliations, analyze monthly spending, support tax controls and monthly close, and improve accounting processes. The role also tracks documentation and tasks using collaboration tools in a remote startup environment.

Lead client-facing AI and machine learning implementations from architecture through production deployment. Build scalable ML pipelines, MLOps workflows, Generative AI and agentic applications, RAG systems, and distributed data-processing solutions using Databricks, Spark, Docker, and cloud platforms. Translate business needs into technical solutions, manage infrastructure, present findings to stakeholders, support consulting engagements, and contribute to practice growth and knowledge transfer.

Design and develop FastAPI backend services for an AI platform supporting agentic AI services, observability, and MCP servers. Manage relational and non-relational databases, authentication, security, and platform operations. Collaborate across frontend, backend, and AI teams while integrating LangChain, LangGraph, and cloud technologies.

Design and operate AI backend services, agents, SDKs, MCP servers, and unified inference infrastructure. Manage agent lifecycles, observability, security, guardrails, PII masking, RAG pipelines, vector databases, and context optimization. Support internal engineering teams adopting the platform and continuously improve production systems. The role requires strong Python and FastAPI skills, substantial backend experience, and hands-on experience building agentic AI systems.

Owns deployment, configuration, and reliable operation of end-to-end machine learning pipelines on Databricks. Responsibilities include orchestrating Jobs, managing Databricks Asset Bundle deployments across environments and countries, administering MLflow and Unity Catalog model lifecycles, monitoring compute costs and observability, and diagnosing production incidents with development and platform teams. Requires advanced Python, SQL, Delta, Unity Catalog, CI/CD, and infrastructure-as-code experience.

Build and operate production machine learning and data pipelines on Databricks. Responsibilities include orchestrating Jobs workflows, managing Databricks Asset Bundle deployments across environments and countries, administering MLflow and Unity Catalog model lifecycles, monitoring compute costs and observability, and diagnosing runtime incidents. The role requires advanced Python, SQL, Delta, Unity Catalog, CI/CD, Azure DevOps, Git, infrastructure as code, and Databricks platform expertise.

Build and optimize batch and streaming data pipelines, semantic models, and transformations on Databricks using Python and Spark. Define data contracts, quality rules, and data SLOs; guide domain teams on data modeling and product boundaries; mentor engineers and analysts; and improve reusable platform templates, tooling, and engineering practices.

Lead and mentor data scientists while architecting Python-based machine learning solutions, deploying and monitoring production models, applying advanced statistical methods, and translating business problems into actionable data science plans. The role also involves communicating findings to stakeholders, developing junior talent, identifying innovative opportunities, and supporting scalable client delivery.

Design, deploy, monitor, and maintain machine learning models for forecasting, recommendations, pricing, churn, and other business applications. Perform exploratory data analysis, define metrics with stakeholders, build scalable ML workflows, communicate insights, collaborate cross-functionally, mentor junior staff, and promote reproducible data science practices.

Lead end-to-end data migration initiatives across cloud data platforms, including architecture decisions, dependency mapping, cutover planning, validation, risk mitigation, and stakeholder alignment. Build and optimize batch and streaming data pipelines using Databricks, Python, and Spark. Maintain migration documentation, testing strategies, rollback plans, and operational procedures while promoting data governance, quality, and delivery best practices.

Build and optimize batch and streaming data pipelines, semantic models, and transformations on Databricks using Python and Spark. Define data contracts, quality rules, and data SLOs for data products. Guide domain teams on data modeling and product boundaries, mentor engineers and analysts, and improve platform templates, tooling, and engineering practices.

The Data Engineer will design and implement ETL processes, optimize data queries, automate pipelines, and develop scalable data infrastructures.