Join the Analytics team at Wealth.com as a Senior Data Scientist or Senior Analytics Engineer, where you'll engage in insights and analysis, manage and model data in the data warehouse, create reports, and develop data visualizations using SQL and Python, including for ad hoc analyses and basic ELT pipelines.
Use Affirm’s proprietary and other third party data to develop machine learning models that predict the likelihood of default and make an approval or decline decision to achieve business objectives. Partner with platform and product engineering teams to build model training, decisioning, and monitoring systems. Research ground breaking solutions and develop prototypes that drive the future of credit decisioning at Affirm.
The Data Engineer is a hands-on role responsible for building scalable data pipelines and developing automated reporting solutions. Requires strong technical skills, problem-solving ability, and effective communication with both technical and non-technical stakeholders. Ideal for a self-starter who thrives in a fast-paced environment and takes ownership of delivering results.
As a Manager Data Scientist, you will lead a team focused on developing advanced recommendation engines and deploying machine learning models into production environments. You will leverage your expertise in Python and PySpark coding, along with your experience in MLOps, to play a pivotal role in enhancing Blend's data-driven decision-making processes. This position offers a unique opportunity to lead and mentor a team while driving technical excellence and innovation.
This role as Manager of Healthcare Data Engineering is within CommonSpirit’s Payer Strategy & Analytics (PSA) team. As the Manager, you will manage and optimize PSA’s data repositories, tools and applications to support the delivery of exceptional patient care through contracting excellence. Your leadership and expertise in data engineering will be pivotal in driving innovation and efficiency within our organization.
Your primary responsibilities will include building tools and products that enhance data science productivity, enable self-serve analytics, and ensure data reliability and quality. This role deals specifically with blockchain data. Design data models, build tooling and pipelines for blockchain data, and build mechanisms of data quality measurement and enforcement to ensure reliability and completeness of data.
Hands on Data Engineer with some architecture skills to operate in a lead role. The role involves understanding the underlying business problem to enable and deliver a technical solution. Heavy experience in asset management/financial services is needed. A strong background working within GCP. Strong experience working with Snowflake or Redshift. Specific experience coding in Python is needed.
The Staff Data Analyst - Digital Success role is an exciting opportunity to help scale and measure customer insights that maximize value across our global customer base. You will lead data-focused initiatives that deliver insights to our customers at scale, capture insights to reduce churn and drive growth, and measure impact across Digital Success programs to drive business planning and investment decisions.
You will have end-to-end ownership of generating insights, developing custom models and building critical analytics products. In collaboration with a highly skilled team of engineers, data scientists, and blockchain analysts, your goal will be to uncover valuable and defensible information that can be used to detect, prevent, and mitigate cryptocurrency fraud and financial crime. The models and analysis you create will be specifically designed to identify new threat vectors that are unique to cryptocurrencies and blockchains.
An experienced data engineer is needed to transform raw data into useful data systems. You will be responsible for designing, developing, testing, and deploying data pipelines, data warehouses, data lakes, and data products that support business needs. You will also work closely with data analysts, data scientists, and other stakeholders to ensure data quality, reliability, and availability.