HR at Fractal Analytics
Views:3112 Applications:410 Rec. Actions:Recruiter Actions:3
Fractal Analytics - Data Scientist - CPG Domain - IIT/NIT/BITS (3-8 yrs)
Fractal Analytics is actively involved in helping Fortune 500 companies by enabling them to discover how they can leverage the data that they generate using advanced and sophisticated algorithms from Artificial Intelligence and Machine Learning. Additionally, the group is also involved in creating a rich and diverse IP portfolio within Artificial Intelligence, Machine Learning, and Big Data
We are looking for Data Scientists with the capability to perform independent statistical and machine learning research/ projects. As a data scientist, you will be involved in using state of the art machine learning algorithms to solve challenging high-impact business problems. If you are a problem solver with a curiosity for exploring new techniques and technologies in AI & ML space, then we would like to talk with you.
Some of the attractive pluses of the job are:
The opportunity to work with a world-class team of talented data scientists with a strong background in machine learning, and who are extremely passionate about solving real-world applied problems in the various business domains.
A real opportunity to expand your skill-sets and a chance to learn and use the latest advances and approaches in machine learning and data mining on real-world datasets
Working at a fast-paced work environment where you are expected to be self-motivated and are measured on your performance.
- Ability to understand a problem statement and implement analytical solutions & techniques independently or with minimal supervision
- Work and collaborate with other teams to deliver and create value for clients
- Fast learner: ability to learn and pick up a new language/tool/ platform quickly
- Conceptualize, design and deliver high-quality solutions and insightful analysis
- Conduct research and prototyping innovations; data and requirements gathering; solution scoping and architecture; consulting clients and client-facing teams on advanced statistical and machine learning problems.
- Ability to deliver AIML based solutions around a host of domains and problems, with some of them being: Customer -Segmentation & Targeting, Propensity Modeling, Churn Modeling, Lifetime Value Estimation, Forecasting, Recommender Systems, Modeling Response to Incentives, Marketing Mix Optimization, Price Optimization
- Intermediate to expert level proficiency in at least one of R and Python
- Ability to discover effective solutions to complex problems. Strong skills in data structures and algorithms.
- Experience of working on a project end-to-end: problem scoping, data gathering, EDA, modelling, insights, and visualizations
- Problem-solving: Ability to break the problem into small problems and think of relevant techniques which can be explored & used to cater to those
- Intermediate to advanced knowledge of machine learning, probability theory, statistics, and algorithms. You will be required to discuss and use various algorithms and approaches on a daily basis.
- We use regression, Bayesian methods, tree-based learners, SVM, RF, XGBOOST, time series modelling, dimensionality reduction, SEM, GLM, GLMM, clustering etc on a regular basis. If you know a few of them you are good to go.
Good to Have:
- Experience in one of the upcoming technologies like deep learning, NLP, image processing, recommender systems
- The experience of working in on one or more domains:
CPG: pricing and promotion analytics, marketing analytics, trade promotions, supply chain management
BFSI: cross-sell, up-sell, campaign analytics, treasury analytics, fraud detection
- Healthcare: medical adherence, medical risk profiling, EHR data, fraud-waste-abuse
- Experience in working with Linux computing environment and use of command-line tools like sed/awk
- Grasp at databases including RDBMS, NoSQL, MongoDB etc.
B.Tech / M.Tech in Computer Science / Mathematics / Signal Processing or related fields from one of the premier Institutes (IITs/NITs/BITS)
This job opening was posted long time back. It may not be active. Nor was it removed by the recruiter. Please use your discretion.