Satori Analytics

Backend Engineer

• Develop high-quality software that meets requirements
• Conduct unit tests
• Enhance code stability
• Collaborate with Solution Architects, Data Engineers, and clients
• Participate in daily stand-ups and design sessions
• Analyze and implement software mechanisms that improve performance and reusability
• Document cloud-based solutions

Python Developer

Data Scientist

• Work with stakeholders to understand business problems and contribute to solutions using machine learning and optimization
• Analyze messy data from various data sources
• Build robust supervised and unsupervised machine-learning pipelines with support and guidance from senior team members
• Maintain and improve existing machine-learning solutions
• Design, build, and operate scalable data platforms for marketing-services products of a global payments and financial-services leader
• Enable campaign measurement, customer insights, reporting, and AI/ML initiatives
• Partner with Product, Engineering, Data Science, and Business stakeholders
• Deliver secure, reliable, high-quality solutions at enterprise scale

• Work with stakeholders to understand business problems and contribute to solving them with ML/Optimization.
• Analyze messy data from various data sources.
• Build robust supervised/unsupervised ML pipelines with support and guidance from senior team members.
• Maintain and improve existing ML solutions.
• Design, build and operate scalable data platforms for marketing services products of a global payments and financial-services leader.
• Enable campaign measurement, customer insights, reporting and AI/ML initiatives.
• Partner with Product, Engineering, Data Science and Business stakeholders.
• Deliver secure, reliable and high-quality solutions at enterprise scale.

Junior Data Scientist

Machine Learning Engineer

• Design and maintain infrastructure that takes ML models from experimentation to reliable, scalable deployment.
• Create repeatable workflows for training, evaluation, deployment, and retraining, with versioning and reproducibility.
• Package and deploy models as production services across cloud, on-premise, or hybrid environments.
• Monitor model performance, data drift, system health, and production signals.
• Collaborate with Data Scientists, AI Engineers, and Software Engineers to improve model testing, deployment, and maintenance.
• Contribute to best practices for CI/CD, model registry, observability, security, and governance.
• Deploy and optimize LLM-based systems, including inference services, GPU usage, and RAG infrastructure.
• Ensure ML deployments follow strong access control, data governance, security, and operational resilience practices.

Senior MLOps Engineer

Data Engineer

• Design, build, and operate scalable data platforms for marketing products.
• Enable campaign measurement, customer insights, reporting, and AI/ML initiatives.
• Independently design and maintain scalable ETL pipelines delivering clean, analytics- and AI-ready data.
• Develop, optimize, and automate data processes using SQL, Python, PySpark, MS Fabric, Azure Data Factory, Databricks, and Snowflake.
• Design robust ETL processes, scalable data models, and optimized design patterns for analytics and BI workloads.
• Ensure data quality and governance through automated checks, monitoring, source-to-target mapping, data lineage, and continuous improvements.
• Collaborate with Product, Engineering, Data Science, and Business stakeholders to understand requirements and business semantics.
• Troubleshoot, optimize, and support production pipelines.
• Use Git and Agile practices in collaborative, iterative projects.

AI Engineer

• Design and develop AI workflows and/or models in Python, focusing on LLMs.
• Build scalable solutions bridging AI/ML and software engineering.
• Collaborate with data scientists, developers, and domain experts.
• Research new trends and integrate cutting-edge techniques.
• Translate business needs into practical ML/AI solutions.
• Communicate results clearly.
• Document work to ensure reproducibility and team understanding.

• Design, build, and operate scalable data platforms behind a global payments and financial-services leader’s marketing services products
• Enable campaign measurement, customer insights, reporting, and AI/ML initiatives
• Partner with Product, Engineering, Data Science, and Business stakeholders
• Define business problems and translate them into data science solutions
• Collect, clean, and analyze large datasets
• Develop and optimize machine learning and statistical models
• Communicate analytical findings and recommendations to stakeholders
• Understand business problems and contribute to solutions using ML/Optimization
• Analyze messy data from various data sources
• Build robust supervised/unsupervised ML pipelines with support and guidance from senior team members
• Maintain and improve existing ML solutions