The role is with a Fintech Credit Card company based in Pune within the Decision Science team. (OneCard )
About
Credit cards haven't changed much for over half a century so our team of seasoned bankers, technologists, and designers set out to redefine the credit card for you - the consumer. The result is OneCard - a credit card reimagined for the mobile generation. OneCard is India's best metal credit card built with full-stack tech. It is backed by the principles of simplicity, transparency, and giving back control to the user.
The Engineering Challenge
“Re-imaging credit and payments from First Principles”
Payments is an interesting engineering challenge in itself with requirements of low latency, transactional guarantees, security, and high scalability. When we add credit and engagement into the mix, the challenge becomes even more interesting with underwriting and recommendation algorithms working on large data sets. We have eliminated the current call center, sales agent, and SMS-based processes with a mobile app that puts the customers in complete control. To stay agile, the entire stack is built on the cloud with modern technologies.
Purpose of Role :
- Develop and implement the collection analytics and strategy function for the credit cards. Use analysis and customer insights to develop optimum strategy.
CANDIDATE PROFILE :
- Successful candidates will have in-depth knowledge of statistical modelling/data analysis tools (Python, R etc.), techniques. They will be an adept communicator with good interpersonal skills to work with senior stake holders in India to grow revenue primarily through identifying / delivering / creating new, profitable analytics solutions.
We are looking for someone who:
- Proven track record in collection and risk analytics preferably in Indian BFSI industry. This is a must.
- Identify & deliver appropriate analytics solutions
- Experienced in Analytics team management
Essential Duties and Responsibilities :
- Responsible for delivering high quality analytical and value added services
- Responsible for automating insights and proactive actions on them to mitigate collection Risk.
- Work closely with the internal team members to deliver the solution
- Engage Business/Technical Consultants and delivery teams appropriately so that there is a shared understanding and agreement as to deliver proposed solution
- Use analysis and customer insights to develop value propositions for customers
- Maintain and enhance the suite of suitable analytics products.
- Actively seek to share knowledge within the team
- Share findings with peers from other teams and management where required
- Actively contribute to setting best practice processes.
Knowledge, Experience and Qualifications :
Knowledge :
- Good understanding of collection analytics preferably in Retail lending industry.
- Knowledge of statistical modelling/data analysis tools (Python, R etc.), techniques and market trends
- Knowledge of different modelling frameworks like Linear Regression, Logistic Regression, Multiple Regression, LOGIT, PROBIT, time- series modelling, CHAID, CART etc.
- Knowledge of Machine learning & AI algorithms such as Gradient Boost, KNN, etc.
- Understanding of decisioning and portfolio management in banking and financial services would be added advantage
- Understanding of credit bureau would be an added advantage
Experience :
- 4 to 8 years of work experience in core analytics function of a large bank / consulting firm.
- Experience on working on Collection analytics is must
- Experience on handling large data volumes using data analysis tools and generating good data insights
- Demonstrated ability to communicate ideas and analysis results effectively both verbally and in writing to technical and non-technical audiences
- Excellent communication, presentation and writing skills Strong interpersonal skills
- Motivated to meet and exceed stretch targets
- Ability to make the right judgments in the face of complexity and uncertainty
- Excellent relationship and networking skills across our different business and geographies
Qualifications :
- Masters degree in Statistics, Mathematics, Economics, Business Management or Engineering from a reputed college
About fintech startup
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Carsome’s Data Department is on the lookout for a Data Scientist/Senior Data Scientist who has a strong passion in building data powered products.
Data Science function under the Data Department has a responsibility for standardisation of methods, mentoring team of data science resources/interns, including code libraries and documentation, quality assurance of outputs, modeling techniques and statistics, leveraging a variety of technologies, open-source languages, and cloud computing platform.
You will get to lead & implement projects such as price optimization/prediction, enabling iconic personalization experiences for our customer, inventory optimization etc.
Job Descriptions
- Identifying and integrating datasets that can be leveraged through our product and work closely with data engineering team to develop data products.
- Execute analytical experiments methodically to help solve various problems and make a true impact across functions such as operations, finance, logistics, marketing.
- Identify, prioritize, and design testing opportunities that will inform algorithm enhancements.
- Devise and utilize algorithms and models to mine big data stores, perform data and error analysis to improve models and clean and validate data for uniformity and accuracy.
- Unlock insights by analyzing large amounts of complex website traffic and transactional data.
- Implement analytical models into production by collaborating with data analytics engineers.
Technical Requirements
- Expertise in model design, training, evaluation, and implementation ML Algorithm expertise K-nearest neighbors, Random Forests, Naive Bayes, Regression Models. PyTorch, TensorFlow, Keras, deep learning expertise, tSNE, gradient boosting expertise, regression implementation expertise, Python, Pyspark, SQL, R, AWS Sagemaker /personalize etc.
- Machine Learning / Data Science Certification
Experience & Education
- Bachelor’s in Engineering / Master’s in Data Science / Postgraduate Certificate in Data Science.
ABOUT EPISOURCE:
Episource has devoted more than a decade in building solutions for risk adjustment to measure healthcare outcomes. As one of the leading companies in healthcare, we have helped numerous clients optimize their medical records, data, analytics to enable better documentation of care for patients with chronic diseases.
The backbone of our consistent success has been our obsession with data and technology. At Episource, all of our strategic initiatives start with the question - how can data be “deployed”? Our analytics platforms and datalakes ingest huge quantities of data daily, to help our clients deliver services. We have also built our own machine learning and NLP platform to infuse added productivity and efficiency into our workflow. Combined, these build a foundation of tools and practices used by quantitative staff across the company.
What’s our poison you ask? We work with most of the popular frameworks and technologies like Spark, Airflow, Ansible, Terraform, Docker, ELK. For machine learning and NLP, we are big fans of keras, spacy, scikit-learn, pandas and numpy. AWS and serverless platforms help us stitch these together to stay ahead of the curve.
ABOUT THE ROLE:
We’re looking to hire someone to help scale Machine Learning and NLP efforts at Episource. You’ll work with the team that develops the models powering Episource’s product focused on NLP driven medical coding. Some of the problems include improving our ICD code recommendations, clinical named entity recognition, improving patient health, clinical suspecting and information extraction from clinical notes.
This is a role for highly technical data engineers who combine outstanding oral and written communication skills, and the ability to code up prototypes and productionalize using a large range of tools, algorithms, and languages. Most importantly they need to have the ability to autonomously plan and organize their work assignments based on high-level team goals.
You will be responsible for setting an agenda to develop and ship data-driven architectures that positively impact the business, working with partners across the company including operations and engineering. You will use research results to shape strategy for the company and help build a foundation of tools and practices used by quantitative staff across the company.
During the course of a typical day with our team, expect to work on one or more projects around the following;
1. Create and maintain optimal data pipeline architectures for ML
2. Develop a strong API ecosystem for ML pipelines
3. Building CI/CD pipelines for ML deployments using Github Actions, Travis, Terraform and Ansible
4. Responsible to design and develop distributed, high volume, high-velocity multi-threaded event processing systems
5. Knowledge of software engineering best practices across the development lifecycle, coding standards, code reviews, source management, build processes, testing, and operations
6. Deploying data pipelines in production using Infrastructure-as-a-Code platforms
7. Designing scalable implementations of the models developed by our Data Science teams
8. Big data and distributed ML with PySpark on AWS EMR, and more!
BASIC REQUIREMENTS
-
Bachelor’s degree or greater in Computer Science, IT or related fields
-
Minimum of 5 years of experience in cloud, DevOps, MLOps & data projects
-
Strong experience with bash scripting, unix environments and building scalable/distributed systems
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Experience with automation/configuration management using Ansible, Terraform, or equivalent
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Very strong experience with AWS and Python
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Experience building CI/CD systems
-
Experience with containerization technologies like Docker, Kubernetes, ECS, EKS or equivalent
-
Ability to build and manage application and performance monitoring processes
- Develop, train, and optimize machine learning models using Python, ML algorithms, deep learning frameworks (e.g., TensorFlow, PyTorch), and other relevant technologies.
- Implement MLOps best practices, including model deployment, monitoring, and versioning.
- Utilize Vertex AI, MLFlow, KubeFlow, TFX, and other relevant MLOps tools and frameworks to streamline the machine learning lifecycle.
- Collaborate with cross-functional teams to design and implement CI/CD pipelines for continuous integration and deployment using tools such as GitHub Actions, TeamCity, and similar platforms.
- Conduct research and stay up-to-date with the latest advancements in machine learning, deep learning, and MLOps technologies.
- Provide guidance and support to data scientists and software engineers on best practices for machine learning development and deployment.
- Assist in developing tooling strategies by evaluating various options, vendors, and product roadmaps to enhance the efficiency and effectiveness of our AI and data science initiatives.
Graas uses predictive AI to turbo-charge growth for eCommerce businesses. We are “Growth-as-a-Service”. Graas is a technology solution provider using predictive AI to turbo-charge growth for eCommerce businesses. Graas integrates traditional data silos and applies a machine-learning AI engine, acting as an in-house data scientist to predict trends and give real-time insights and actionable recommendations for brands. The platform can also turn insights into action by seamlessly executing these recommendations across marketplace store fronts, brand.coms, social and conversational commerce, performance marketing, inventory management, warehousing, and last mile logistics - all of which impacts a brand’s bottom line, driving profitable growth.
Roles & Responsibilities:
Work on implementation of real-time and batch data pipelines for disparate data sources.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS technologies.
- Build and maintain an analytics layer that utilizes the underlying data to generate dashboards and provide actionable insights.
- Identify improvement areas in the current data system and implement optimizations.
- Work on specific areas of data governance including metadata management and data quality management.
- Participate in discussions with Product Management and Business stakeholders to understand functional requirements and interact with other cross-functional teams as needed to develop, test, and release features.
- Develop Proof-of-Concepts to validate new technology solutions or advancements.
- Work in an Agile Scrum team and help with planning, scoping and creation of technical solutions for the new product capabilities, through to continuous delivery to production.
- Work on building intelligent systems using various AI/ML algorithms.
Desired Experience/Skill:
- Must have worked on Analytics Applications involving Data Lakes, Data Warehouses and Reporting Implementations.
- Experience with private and public cloud architectures with pros/cons.
- Ability to write robust code in Python and SQL for data processing. Experience in libraries such as Pandas is a must; knowledge of one of the frameworks such as Django or Flask is a plus.
- Experience in implementing data processing pipelines using AWS services: Kinesis, Lambda, Redshift/Snowflake, RDS.
- Knowledge of Kafka, Redis is preferred
- Experience on design and implementation of real-time and batch pipelines. Knowledge of Airflow is preferred.
- Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
Role :
- Understand and translate statistics and analytics to address business problems
- Responsible for helping in data preparation and data pull, which is the first step in machine learning
- Should be able to do cut and slice data to extract interesting insights from the data
- Model development for better customer engagement and retention
- Hands on experience in relevant tools like SQL(expert), Excel, R/Python
- Working on strategy development to increase business revenue
Requirements:
- Hands on experience in relevant tools like SQL(expert), Excel, R/Python
- Statistics: Strong knowledge of statistics
- Should able to do data scraping & Data mining
- Be self-driven, and show ability to deliver on ambiguous projects
- An ability and interest in working in a fast-paced, ambiguous and rapidly-changing environment
- Should have worked on Business Projects for an organization, Ex: customer acquisition, Customer retention.
About the Company:
This opportunity is for an AI Drone Technology startup funded by the Indian Army. It is working to develop cutting-edge products to help the Indian Army gain an edge in New Age Enemy Warfare.
They are working on using drones to neutralize terrorists hidden in deep forests. Get a chance to contribute to secure our borders against the enemy.
Responsibilities:
- Extensive knowledge in machine learning and deep learning techniques
- Solid background in image processing/computer vision
- Experience in building datasets for computer vision tasks
- Experience working with and creating data structures/architectures
- Proficiency in at least one major machine learning framework such as Tensorflow, Pytorch
- Experience visualizing data to stakeholders
- Ability to analyze and debug complex algorithms
- Highly skilled in Python scripting language
- Creativity and curiosity for solving highly complex problems
- Excellent communication and collaboration skills
Educational Qualification:
MS in Engineering, Applied Mathematics, Data Science, Computer Science or equivalent field, with 3 years industry experience, a PhD degree or equivalent industry experience.
Only a solid grounding in computer engineering, Unix, data structures and algorithms would enable you to meet this challenge. 7+ years of experience architecting, developing, releasing, and maintaining large-scale big data platforms on AWS or GCP Understanding of how Big Data tech and NoSQL stores like MongoDB, HBase/HDFS, ElasticSearch synergize to power applications in analytics, AI and knowledge graphs Understandingof how data processing models, data location patterns, disk IO, network IO, shuffling affect large scale text processing - feature extraction, searching etc Expertise with a variety of data processing systems, including streaming, event, and batch (Spark, Hadoop/MapReduce) 5+ years proficiency in configuring and deploying applications on Linux-based systems 5+ years of experience Spark - especially Pyspark for transforming large non-structured text data, creating highly optimized pipelines Experience with RDBMS, ETL techniques and frameworks (Sqoop, Flume) and big data querying tools (Pig, Hive) Stickler of world class best practices, uncompromising on the quality of engineering, understand standards and reference architectures and deep in Unix philosophy with appreciation of big data design patterns, orthogonal code design and functional computation models |
About us
DataWeave provides Retailers and Brands with “Competitive Intelligence as a Service” that enables them to take key decisions that impact their revenue. Powered by AI, we provide easily consumable and actionable competitive intelligence by aggregating and analyzing billions of publicly available data points on the Web to help businesses develop data-driven strategies and make smarter decisions.
Data Science@DataWeave
We the Data Science team at DataWeave (called Semantics internally) build the core machine learning backend and structured domain knowledge needed to deliver insights through our data products. Our underpinnings are: innovation, business awareness, long term thinking, and pushing the envelope. We are a fast paced labs within the org applying the latest research in Computer Vision, Natural Language Processing, and Deep Learning to hard problems in different domains.
How we work?
It's hard to tell what we love more, problems or solutions! Every day, we choose to address some of the hardest data problems that there are. We are in the business of making sense of messy public data on the web. At serious scale!
What do we offer?
● Some of the most challenging research problems in NLP and Computer Vision. Huge text and image
datasets that you can play with!
● Ability to see the impact of your work and the value you're adding to our customers almost immediately.
● Opportunity to work on different problems and explore a wide variety of tools to figure out what really
excites you.
● A culture of openness. Fun work environment. A flat hierarchy. Organization wide visibility. Flexible
working hours.
● Learning opportunities with courses and tech conferences. Mentorship from seniors in the team.
● Last but not the least, competitive salary packages and fast paced growth opportunities.
Who are we looking for?
The ideal candidate is a strong software developer or a researcher with experience building and shipping production grade data science applications at scale. Such a candidate has keen interest in liaising with the business and product teams to understand a business problem, and translate that into a data science problem.
You are also expected to develop capabilities that open up new business productization opportunities.
We are looking for someone with a Master's degree and 1+ years of experience working on problems in NLP or Computer Vision.
If you have 4+ years of relevant experience with a Master's degree (PhD preferred), you will be considered for a senior role.
Key problem areas
● Preprocessing and feature extraction noisy and unstructured data -- both text as well as images.
● Keyphrase extraction, sequence labeling, entity relationship mining from texts in different domains.
● Document clustering, attribute tagging, data normalization, classification, summarization, sentiment
analysis.
● Image based clustering and classification, segmentation, object detection, extracting text from images,
generative models, recommender systems.
● Ensemble approaches for all the above problems using multiple text and image based techniques.
Relevant set of skills
● Have a strong grasp of concepts in computer science, probability and statistics, linear algebra, calculus,
optimization, algorithms and complexity.
● Background in one or more of information retrieval, data mining, statistical techniques, natural language
processing, and computer vision.
● Excellent coding skills on multiple programming languages with experience building production grade
systems. Prior experience with Python is a bonus.
● Experience building and shipping machine learning models that solve real world engineering problems.
Prior experience with deep learning is a bonus.
● Experience building robust clustering and classification models on unstructured data (text, images, etc).
Experience working with Retail domain data is a bonus.
● Ability to process noisy and unstructured data to enrich it and extract meaningful relationships.
● Experience working with a variety of tools and libraries for machine learning and visualization, including
numpy, matplotlib, scikit-learn, Keras, PyTorch, Tensorflow.
● Use the command line like a pro. Be proficient in Git and other essential software development tools.
● Working knowledge of large-scale computational models such as MapReduce and Spark is a bonus.
● Be a self-starter—someone who thrives in fast paced environments with minimal ‘management’.
● It's a huge bonus if you have some personal projects (including open source contributions) that you work
on during your spare time. Show off some of your projects you have hosted on GitHub.
Role and responsibilities
● Understand the business problems we are solving. Build data science capability that align with our product strategy.
● Conduct research. Do experiments. Quickly build throw away prototypes to solve problems pertaining to the Retail domain.
● Build robust clustering and classification models in an iterative manner that can be used in production.
● Constantly think scale, think automation. Measure everything. Optimize proactively.
● Take end to end ownership of the projects you are working on. Work with minimal supervision.
● Help scale our delivery, customer success, and data quality teams with constant algorithmic improvements and automation.
● Take initiatives to build new capabilities. Develop business awareness. Explore productization opportunities.
● Be a tech thought leader. Add passion and vibrance to the team. Push the envelope. Be a mentor to junior members of the team.
● Stay on top of latest research in deep learning, NLP, Computer Vision, and other relevant areas.