Data Scientist

Responsibilities

• Analysing raw data: assessing quality, cleansing,
structuring for downstream processing

• Designing accurate and scalable prediction algorithms for
different Analytical models including Customer Happiness Index, Competitor
Alerts, Customer Segmentation, Footfall Tracking and Social Media Monitoring

• Designing AI-driven e-Commerce, Campaign Management and
Loyalty Solutions

• Generating actionable insights for business improvements
and feeding the data to reporting tools

 • Maintaining
up-to-date knowledge of research activities in the general fields of machine
learning and specifically for map feature inference

• Utilising computer vision and stochastic algorithms to
solve and implement complex attribute engineering problems for machine learning
and deep learning based mobile frameworks and products.

• Participating in both algorithm and software developments
as a part of a scrum team, and contribute artifacts (software, whitepaper,
datasets) for internal and external project reviews and demos.

• Collaborating with internal and external team members
(researchers and engineers) on efficiently implementing the new features to the
products or enhancing the existing features. With end-to-tend aspects like
developing, testing, and deploying.

Experience Required

 • Development
experience with and Scala

• Development experience with Docker

 • Development
experience with GIS data

• Development experience with NoSQL (i.e. MongoDB)

• Cognitive Networks (Markovian & Bayesian Networks,
Deep neural networks, Kmean Clustering, Graph analysis) • Cognitive Analytics (Visual
Sentiment Analysis, Emotion Analysis)

• Behavioural Analytics (Anomaly Detection Tools,
Recommender Tools)

• Spatiotemporal Analytics (Spatiotemporal Data Mining,
Spatiotemporal Indexing)

• Proficiency with at-least one of the deep learning
frameworks like Tensorflow, Keras and Pytorch

 • Proficiency with
at-least one of the mobile deep learning inference engines like TFLite, CoreML

 • Programming
experience with SQL, shell script, Python, etc.

• Familiarity with Big Data frameworks and visualisation tools
(Kibana, Scala, Cloud Dataproc Cassandra, Hadoop, Spark, Tableau)