Director MLE

1 month ago

Bengaluru, India Glance Full time
Glance - An InMobi Group Company
Founded in 2019, Glance is a consumer technology company that operates disruptive digital platforms including Glance, Roposo, and Nostra. Glance’s ’smart lock screen’ inspires consumers to make the most of every moment by surfacing relevant experiences without the need for searching and downloading apps. Glance Lock Screen is currently available on over 450 million smartphones worldwide.
Incorporated 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, nostra.gg, and roposo.com
Machine learning at Glance:
Machine learning engineers at Glance work on a variety of Data Science, Machine Learning, and AI-based problem statements. From a core technology perspective, this includes Recommendation systems, Natural Language Processing including sentiment analysis and abstractive text summarization, Image processing such as feature extraction, Prediction tasks relating to conversion and engagement rates, alongside many other Data Science focus areas.
Recommendation Systems (Personalisation) and Predictions relating to Engagement / Conversion for Monetisation are two critical key focus areas for us.
We collaborate externally with Academic and Commercial experts in the field and have started to push our paper publication initiative. This allows us to explore research opportunities that have significant upside potential whilst staying focused on core business areas.
Role overview:
You will drive the overall vision and strategy for the AdMonetization team at Glance and will play a transformational role in driving the larger AI-first aspirations of the company. AdMonetization team at Glance is responsible 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. Know how of Ad-tech & pricing strategies is critical for this role.
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 data science 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.
10+ years of ad tech related industry experience working in ML / DS teams. You would have applied algorithms and techniques such as NLP, Reinforcement Learning, Time Series, etc. from Machine Learning, Deep Learning, and Statistics or other domains in solving real world problems and understand the practical issues of using these algorithms especially on large datasets.
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. 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

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