Job Details
Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets; an ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work Institute and recognized as a Cool Vendor and a Vendor to Watch by Gartner.
Please visit Fractal | Intelligence for Imagination for more information about Fractal.
Role Overview
The Artificial Intelligence and Machine Learning (AIML) group at Fractal Analytics is actively involved in helping Fortune 500 companies by enabling them to discover how they can leverage their data using advanced and sophisticated AI/ML algorithms for which we are looking for Data Scientists with the capability to work on independent statistical and machine learning research/ projects. If you are a problem solver curious about exploring new techniques and technologies in AIML space, then we would like to talk with you.
Job Responsibilities
- Ability to understand a problem statement and implement analytical solutions and techniques independently, proactively, and with thought leadership.
- Work with organizational stakeholders to identify opportunities for leveraging company/client data to drive business solutions.
- Fast learner: ability to learn and quickly learn a new language/tool/ platform.
- Conceptualize, design, and deliver high-quality solutions and insightful analysis.
- Conduct research and prototyping innovations, data and requirements gathering, solution scoping and architecture, and consulting clients and clients-facing teams on advanced statistical and machine learning problems.
- Collaborate and Coordinate with different functional teams (engineering and product development) to implement models and monitor outcomes.
- 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
Experience Required
- Expert level proficiency in at least one of R and Python with a focus on object-oriented programming.
- Ability to create efficient solutions to complex problems. Strong skills in data-structures and ML algorithms.
- Demonstrable understanding of machine learning techniques and their practical applications.
- Excellent collaboration and communication skills.
- A team player with a proactive approach to problem-solving.
- Familiarity with NLP and text analysis.
- Experience working on end-to-end data science pipeline: problem scoping, data gathering, EDA, modeling, insights, visualizations, monitoring, and maintenance.
- Problem-solving: Ability to break the problem into small parts and apply relevant techniques to drive required outcomes.
- Intermediate to advanced knowledge of machine learning, probability theory, statistics, and algorithms. You will be required to discuss and use various algorithms and approaches daily.
- We use regression, Bayesian methods, tree-based learners, SVM, RF, XGBOOST, time series modeling, dimensionality reduction, SEM, GLM, GLMM, clustering, Deep learning etc. regularly. If you know a few of them, you are good to go.
Good to Have:
- Experience in upcoming technologies like deep learning, NLP, image processing, and recommender systems.
- Experience working in 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 working with Linux computing environment and using command line tools like sed/awk.
- Good grasp of databases, including RDBMS, NoSQL, MongoDB, etc.