Associate Data Scientist Data Axle - Inboxable

  • company name Data Axle - Inboxable
  • working location Office Location
  • job type Full Time

Experience: 2 - 2 years required

Pay:

Salary Information not included

Type: Full Time

Location: Maharashtra

Skills: data science, Machine Learning, Python, model design, Data Visualization, MLOps, Cloud architecture, Data Ingestion, Model Inference, Peer review

About Data Axle - Inboxable

Job Description

Data Axle Inc. has been an industry leader in data, marketing solutions, sales, and research for over 50 years in the USA. Data Axle now has an established strategic global centre of excellence in Pune. This centre delivers mission critical data services to its global customers powered by its proprietary cloud-based technology platform and by leveraging proprietary business and consumer databases. Data Axle India is recognized as a Great Place to Work! This prestigious designation is a testament to our collective efforts in fostering an exceptional workplace culture and creating an environment where every team member can thrive. Roles & Responsibilities We are looking for Associate Data Scientist to join the Data Science Client Services team to continue our success of identifying high quality target audiences that generate profitable marketing return for our clients. We are looking for experienced data science, machine learning and MLOps practitioners to design, build and deploy impactful predictive marketing solutions that serve a wide range of verticals and clients. The right candidate will enjoy contributing to and learning from a highly talented team and working on a variety of projects. Ownership of design, implementation, and deployment of machine learning algorithms in a modern Python-based cloud architecture. Design or enhance ML workflows for data ingestion, model design, model inference and scoring. Oversight on team project execution and delivery. Establish peer review guidelines for high quality coding to help develop junior team members" skill set growth, cross-training, and team efficiencies. Visualize and publish model performance results and insights to internal and external audiences.,