Experience: 10+ years
About the Role:
As a Data Scientist at ElectrifAi, you will be part of a global team of data scientists working on products and solutions for Fortune 500 companies. You will improve upon and build new ML use cases for our customers world-wide. You will collaborate with a multi-disciplinary team of data scientists, software engineers, analysts and product management on a wide range of problems such as dynamic pricing, customer 360, demand forecasting, spend analytics, NLP solutions etc.
- Work to create new deep learning and ML algorithms and applications. Drive technical vision and strategy to make ML a must-have in delivering solutions.
- Help to define and improve large scale Machine Learning solutions that power our big data products to develop personalization, recommendation and predictions.
- Define metrics, conduct A/B testing, and oversee statistical measurement of new algorithms and approaches.
- Use Big Data technologies (such as Hadoop, Spark, Storm) to build large scale data mining pipelines for recommendation and search.
- Apply Natural Language Processing to understand text from forum reviews, description and interactions between users.
- Work with engineering, product management and delivery to build machine learning solutions incorporating data processing, feature engineering and monitoring.
- Design and develop effective models, features, and algorithms involving user activities and interests, blogs and posts, social graphs, etc.
Master's degree in a quantitative discipline (e.g., Statistics, Operations Research, Bioinformatics, Economics, Computational Biology, Computer Science, Mathematics, Physics, Electrical Engineering, Industrial Engineering) or equivalent practical experience.
- 5 years of relevant work experience, including expertise with statistical data analysis such as linear models, multivariate analysis, stochastic models, sampling methods.
- Proven experience in experimentation (a/b, causal inference, synthetic control etc), sophisticated data analysis, and or building machine learning models.
- Strong statistical and machine learning background.
- Experience with statistical software (e.g., R, Python, MATLAB, pandas) and proficiency with SQL and Hive / Spark (experience with streaming data a plus)
- Knowledge of natural language processing techniques and neural networks.
- Experience with Caffe/TensorFlow/Torch or similar deep learning frameworks
Good to have PhD in quantitative discipline