Experience: 3 to 5 years of experience in Data Science
Position Description: 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 with a curiosity for exploring new techniques and technologies in AIML space, then we would like to talk with you.
Ability to understand a problem statement and implement analytical solutions & techniques independently with independently/proactively/thought-leadership.
Work with stakeholders throughout the organization to identify opportunities for leveraging company/client data to drive business solutions.
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.
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
Expert level proficiency in at least one of R and Python.
Ability to create efficient solutions to complex problems. Strong skills in data-structures and ML algorithms.
Experience of working on end-to-end data science pipeline: problem scoping, data gathering, EDA, modelling, insights, visualizations, monitoring and maintenance.
Problem-solving: Ability to break the problem into small parts and applying 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 on a daily basis.
We use regression, Bayesian methods, tree-based learners, SVM, RF, XGBOOST, time series modeling, dimensionality reduction, SEM, GLM, GLMM, clustering, Deep learning etc. on a regular basis. If you know 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