11+ Operations research Jobs in Bangalore (Bengaluru) | Operations research Job openings in Bangalore (Bengaluru)

Job Description

Data scientist with a strong background in data mining, machine learning, recommendation systems, and statistics. Should possess signature strengths of a qualified mathematician with ability to apply concepts of Mathematics, Applied Statistics, with specialisation in one or more of NLP, Computer Vision, Speech, Data mining to develop models that provide effective solution.A strong data engineering background with hands-on coding capabilities is needed to own and deliver outcomes.

A Master’s or PhD Degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent experience, 7+ years of industry experience in predictive modelling, data science and analysis, with prior experience in a ML or data scientist role and a track record of building ML or DL models.

Responsibilities and skills:

• Work with our customers to deliver a ML / DL project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organisation.
• Selecting features, building and optimising classifiers using ML techniques.
• Data mining using state-of-the-art methods, create text mining pipelines to clean & process large unstructured datasets to reveal high quality information and hidden insights using machine learning techniques.
• Should be able to appreciate and work on:

Computer Vision problems – for example extract rich information from images to categorise and process visual data— Develop machine learning algorithms for object and image classification, Experience in using DBScan, PCA, Random Forests and Multinomial Logistic Regression to select the best features to classify objects.

OR

Deep understanding of NLP such as fundamentals of information retrieval, deep learning approaches, transformers, attention models, text summarisation, attribute extraction, etc. Preferable experience in one or more of the following areas: recommender systems, moderation of user generated content, sentiment analysis, etc.

OR

Experience of having worked in these areas : speech recognition, speech to text and vice versa, understanding NLP and IR, text summarisation, statistical and deep learning approaches to text processing.

• Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc. Needs to appreciate deep learning frameworks like MXNet, Caffe 2, Keras, Tensorflow.
• Experience in working with GPUs to develop models, handling terabyte size datasets.
• Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, mlr, mllib, Scikit-learn, caret etc - excellence in at least one of these is highly desirable.
• Should be able to work hands-on in Python, R etc. Should closely collaborate & work with engineering teams to iteratively analyse data using Scala, Spark, Hadoop, Kafka, Storm etc.
• Experience with NoSQL databases and familiarity with data visualisation tools will be of great advantage.

• Bring in industry best practices around creating and maintaining robust data pipelines for complex data projects with/without AI component
  • programmatically ingesting data from several static and real-time sources (incl. web scraping)
  • rendering results through dynamic interfaces incl. web / mobile / dashboard with the ability to log usage and granular user feedbacks
  • performance tuning and optimal implementation of complex Python scripts (using SPARK), SQL (using stored procedures, HIVE), and NoSQL queries in a production environment

• Industrialize ML / DL solutions and deploy and manage production services; proactively handle data issues arising on live apps
• Perform ETL on large and complex datasets for AI applications - work closely with data scientists on performance optimization of large-scale ML/DL model training
• Build data tools to facilitate fast data cleaning and statistical analysis
• Ensure data architecture is secure and compliant
• Resolve issues escalated from Business and Functional areas on data quality, accuracy, and availability
• Work closely with APAC CDO and coordinate with a fully decentralized team across different locations in APAC and global HQ (Paris).

•  Expert in structured and unstructured data in traditional and Big data environments – Oracle / SQLserver, MongoDB, Hive / Pig, BigQuery, and Spark
• Have excellent knowledge of Python programming both in traditional and distributed models (PySpark)
• Expert in shell scripting and writing schedulers
• Hands-on experience with Cloud - deploying complex data solutions in hybrid cloud / on-premise environment both for data extraction/storage and computation
• Hands-on experience in deploying production apps using large volumes of data with state-of-the-art technologies like Dockers, Kubernetes, and Kafka
• Strong knowledge of data security best practices
• 5+ years experience in a data engineering role
• Science / Engineering graduate from a Tier-1 university in the country
• And most importantly, you must be a passionate coder who really cares about building apps that can help people do things better, smarter, and faster even when they sleep

Sizzle is an exciting new startup that’s changing the world of gaming.  At Sizzle, we’re building AI to automate gaming highlights, directly from Twitch and YouTube streams. We’re looking for a superstar engineer that is well versed with AI and audio technologies around audio detection, speech-to-text, interpretation, and sentiment analysis.  

You will be responsible for:

Developing audio algorithms to detect key moments within popular online games, such as:

Streamer speaking, shouting, etc.

Gunfire, explosions, and other in-game audio events

Speech-to-text and sentiment analysis of the streamer’s narration

Leveraging baseline technologies such as TensorFlow and others -- and building models on top of them

Building neural network architectures for audio analysis as it pertains to popular games

Specifying exact requirements for training data sets, and working with analysts to create the data sets

Training final models, including techniques such as transfer learning, data augmentation, etc. to optimize models for use in a production environment

Working with back-end engineers to get all of the detection algorithms into production, to automate the highlight creation

You should have the following qualities:

Solid understanding of AI frameworks and algorithms, especially pertaining to audio analysis, speech-to-text, sentiment analysis, and natural language processing

Experience using Python, TensorFlow and other AI tools

Demonstrated understanding of various algorithms for audio analysis, such as CNNs, LSTM for natural language processing, and others

Nice to have: some familiarity with AI-based audio analysis including sentiment analysis

Familiarity with AWS environments

Excited about working in a fast-changing startup environment

Willingness to learn rapidly on the job, try different things, and deliver results

Ideally a gamer or someone interested in watching gaming content online

Skills:

Machine Learning, Audio Analysis, Sentiment Analysis, Speech-To-Text, Natural Language Processing, Neural Networks, TensorFlow, OpenCV, AWS, Python

Work Experience:  2 years to 10 years

About Sizzle

Sizzle is building AI to automate gaming highlights, directly from Twitch and YouTube videos. Presently, there are over 700 million fans around the world that watch gaming videos on Twitch and YouTube. Sizzle is creating a new highlights experience for these fans, so they can catch up on their favorite streamers and esports leagues. Sizzle is available at http://www.sizzle.gg">www.sizzle.gg. 

Data Scientist

Cubera is a data company revolutionizing big data analytics and Adtech through data share value principles wherein the users entrust their data to us. We refine the art of understanding, processing, extracting, and evaluating the data that is entrusted to us. We are a gateway for brands to increase their lead efficiency as the world moves towards web3.

What you’ll do?

• Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models.
• Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation, and serving.
• Research new and innovative machine learning approaches.
• Perform hands-on analysis and modeling of enormous data sets to develop insights that increase Ad Traffic and Campaign Efficacy.
• Collaborate with other data scientists, data engineers, product managers, and business stakeholders to build well-crafted, pragmatic data products.  
• Actively take on new projects and constantly try to improve the existing models and infrastructure necessary for offline and online experimentation and iteration.
• Work with your team on ambiguous problem areas in existing or new ML initiatives

What are we looking for?

• Ability to write a SQL query to pull the data you need.
• Fluency in Python and familiarity with its scientific stack such as numpy, pandas, scikit learn, matplotlib.
• Experience in Tensorflow and/or R Modelling and/or PyTorch
• Ability to understand a business problem and translate, and structure it into a data science problem. 

Job Category: Data Science

Job Type: Full Time

Job Location: Bangalore

REQUIREMENT:

•  Previous experience of working in large scale data engineering
•  4+ years of experience working in data engineering and/or backend technologies with cloud experience (any) is mandatory.
•  Previous experience of architecting and designing backend for large scale data processing.
•  Familiarity and experience of working in different technologies related to data engineering – different database technologies, Hadoop, spark, storm, hive etc.
•  Hands-on and have the ability to contribute a key portion of data engineering backend.
•  Self-inspired and motivated to drive for exceptional results.
•  Familiarity and experience working with different stages of data engineering – data acquisition, data refining, large scale data processing, efficient data storage for business analysis.
•  Familiarity and experience working with different DB technologies and how to scale them.

RESPONSIBILITY:

•  End to end responsibility to come up with data engineering architecture, design, development and then implementation of it.
•  Build data engineering workflow for large scale data processing.
•  Discover opportunities in data acquisition.
•  Bring industry best practices for data engineering workflow.
•  Develop data set processes for data modelling, mining and production.
•  Take additional tech responsibilities for driving an initiative to completion
•  Recommend ways to improve data reliability, efficiency and quality
•  Goes out of their way to reduce complexity.
•  Humble and outgoing - engineering cheerleaders.