Director Machine Learning - Recommendation Systems

Role Overview

You will drive the vision and strategy for the Recommendation Systems team at Glance and will play a transformational role in driving the larger AI-first aspirations of the company. Recommendations team at Glance plays key role for all Ad revenue – programmatic and direct – on all Glance user experiences. Revenue has been growing steadily, and we’re well placed to make the trajectory steeper. Expertise in designing, building, and operating large-scale recommendation systems is a must.

You will have knowledge of current AI and machine learning capabilities and advances in the field. You should also be interested in the academics of data science but be more focused on practical application within recommendation and personalization technology. You will be able to clearly articulate the purpose of data science solutions to key stakeholders and customers, and then translate those into action for the business. The solutions you and your teams provide will encompass such things as product innovations, create efficiencies and automation across the business, improve data architecture and system architecture, mitigate business risks, and create process improvements.

We are searching for an individual who thrives on describing a vision and inspiring a team to achieve it. We need a leader who will remove obstacles, break barriers, empower, communicate, and engage. Someone who truly harnesses advanced analytic data modelling systems to drive positive outcomes for our customers. From the defining of a strategy to the execution of it, you will also develop, collect, and report the objective metrics required to assure it. You will own driving employee engagement and increasing productivity across the Data Science team and into Engineering.

The Impact You’ll Make

• Define the overall vision for our recommendation applications, focused on up-levelling internal use of machine learning
• Provide technical leadership of overall architecture, ML approaches, performance monitoring, continuing improvement, and production deployments
• Manage, develop, coach, and mentor a team of machine learning engineers and big data specialists
• Partner with our business and product teams to help predict system behaviour, establish metrics, identify bugs, and improve debugging skills
• Ensure data quality and integrity within our products as well as our teams
• Partner with our client-facing teams and customers to enhance products and develop client solutions applying critical thinking skills to remove extraneous inputs
• Conceive, plan, and prioritize data projects
• Lead data mining and collection procedures, especially focused on unstructured and siloed data sets
• Experiment with new models and techniques.
• Drive the implementation of models into Production through various Engineering teams
• Create a positive culture to maximize productivity and minimize attrition

The Experience You'll Need

• PhD or Master’s in a quantitative field such as Computer Science, Electrical Engineering, Statistics, Mathematics, Operations Research or Economics, Analytics, Data Science. Or Bachelor's with additional experience.
• 12+ years of ML related industry experience working on large-scale recommendation systems or personalization. With 5+ years in a leadership role.
• You would bring deep expertise in the applied algorithms, models, and techniques used to design, scale, and optimize large-scale recommendation systems.
• You should be comfortable with software programming and statistical platforms such as Tensorflow, PyTorch, scikit-learn, etc. etc.
• You should be comfortable with using one or more distributed training technologies such as Apache spark, RAPIDS, dask, etc. along with MLOps stack such as kubeflow, mlflow, or their cloud counterparts
• Comfortable collaborating with cross-functional teams.
• Excellent technical and business communication skills and should know how to present technical ideas in a simple manner to business counterparts.
• Possess a high degree of curiosity and ability to rapidly learn new subjects