Director Machine Learning - Recommendation Systems at Glance

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

08 Apr, 26

Salary

0.0

Posted On

08 Jan, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Recommendation Systems, Data Science, Technical Leadership, Data Architecture, Statistical Platforms, Programming, Collaboration, Communication, Curiosity, Data Mining, Model Implementation, Performance Monitoring, Team Management, Client Solutions, Process Improvement

Industry

Software Development

Description

Glance AI is an AI commerce platform shaping the next wave of e-commerce with inspiration-led shopping, less about searching for what you want and more about discovering who you could be. Operating in 140 countries, Glance AI transforms every screen into a stage for instant, personal, and joyful discovery, where inspiration becomes something you can explore, feel, and shop in the moment. Its proprietary models, seamlessly integrated with Google’s most advanced AI platforms, Gemini and Imagen on Vertex AI, deliver hyper-realistic, deeply personal shopping experiences across categories such as fashion, beauty, travel, accessories, home décor, pets, and more. Designed to seamlessly integrate into everyday consumer technology, Glance AI reimagines the future of e-commerce with inspiration-led discovery and shopping. With an open architecture built for effortless adoption across hardware and software ecosystems, Glance AI is creating a platform that can become a staple in everyday consumer technology. It partners with the world’s leading smartphone makers, connected TV manufacturers, telecom providers, and global brands — meeting people where they are: on mobile, smart TVs, and brand websites. Through Glance AI’s rich first-party data and unparalleled consumer access, it harnesses InMobi’s global scale, insights, and targeting capabilities to create high-impact, performance-driven shopping journeys for brands worldwide. Part of the InMobi Group, a global technology and advertising leader reaching over 2 billion devices and serving more than 30,000 enterprise brands worldwide, Glance AI is backed by Google, Jio Platforms, and Mithril Capital. Glance - An InMobi Group Company Founded in 2019, Glance is a consumer technology company that operates some of the most disruptive digital platforms including Glance, Roposo, and Glance TV. Glance has redefined the way the internet is consumed on the lock screen, removing the need to search for and download apps. Over 400 million smartphones now come enabled with Glance’s next-generation internet experience. Roposo has revolutionized commerce by launching a destination for creator-led live entertainment commerce. Glance TV is changing the way consumers engage and interact with their televisions. Headquartered in Singapore, Glance is an unconsolidated subsidiary of InMobi Group and is funded by Jio Platforms, Google, and Mithril Capital. For more information, visit glance.com, roposo.com, and inmobi.com. What should you know about joining Glance? At Glance, we walk the talk – free yourself, dream big, and chase your passion! On joining, you’ll have opportunities to make an immediate impact on mission-critical projects, as you work with highly capable and ambitious peer groups. Be rewarded for your autonomy even as you collaborate. Ideate, innovate, and inspire by leveraging bleeding-edge tech to disrupt consumer experiences. While you work, we’ll take care of nourishing your body, mind, and soul. This includes daily meals, gym, trainings, tech tools, and regular unwind sessions. Also, feel free to bring your kids – even the furry ones – to the office! 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 "Glance collects and processes personal data such as your name, contact details, resume and other information that may contain personal data for the purpose of processing your application. Glance utilizes Greenhouse, a third-party platform. Please review Greenhouse's Privacy Policy to understand how the data collected from you is processed and managed. By clicking on 'Submit Application', you acknowledge and agree to the above privacy terms. Should you have any privacy concerns, you may contact us through the details mentioned in your application confirmation email."

Responsibilities

Drive the vision and strategy for the Recommendation Systems team, focusing on enhancing machine learning applications. Manage and mentor a team while collaborating with business and product teams to improve system performance and data quality.