Senior Data Scientist, Machine Learning

Why Attentive needs you

• Develop data-driven strategies to address key business challenges, such as customer segmentation, churn prediction, and lifetime value optimization
• Use advanced statistical methods and machine learning techniques to analyze and interpret complex data, transforming it into insights that can guide decision-making for both our product and marketing teams
• Partner with product managers to translate business needs into data analysis projects, helping to define KPIs and interpret the results to make actionable recommendations
• Provide guidance on the proper implementation of data science models within our existing software environment, ensuring models are interpretable, robust, and aligned with business objectives
• Collaborate with data engineers and machine learning engineers to develop scalable and reliable data pipelines for model training and inference
• Implement frameworks for monitoring the performance and validity of analytics and machine learning models, ensuring their continued relevance and accuracy

About you

• At least 5 years of professional experience as a Data Scientist, with a specialized focus on software development and advanced analytics
• A proven track record of designing, developing, and deploying software solutions powered by machine learning and data analytics
• Strong expertise in Python, along with proficiency in data manipulation libraries like Pandas and modeling and analysis tools such as NumPy, Scikit-learn, XGBoost, Matplotlib, Seaborn, Scipy
• Demonstrable skill in SQL and experience with database systems like Snowflake, PostgreSQL or MySQL

Our scale

• 8,000 brands powered by Attentive sent over 1.6 billion text messages over Cyber Week 2022 (Black Friday/Cyber Monday) representing a growth of 41% from 2021
• We sent 24 billion SMS messages in 2022. That’s an average of 50 million per day
• Our production cluster contains over 9,000 containers which serve 170+ services
• Our streaming services process over 28 billion events per month

What we use

• Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS
• Infrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and Helm
• Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Pulsar, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS.
• Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright
• Our automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas.