- Bachelor’s degree or higher in Computer Science, Computer Engineering, Economics, Statistics, Mathematics or related discipline. Hands-on experience and project-based learning in computer science, engineering or mathematics is preferred
- Exceptional communication skills are must.
- 2-5 years of strong expertise in machine learning and deep learning
- Exceptional problem-solving skills.
- Strong knowledge on statistical methodologies
- 2+ years professional experience in software development in languages like Java, Python, Scala.
- Data science background and experience manipulating/transforming data, model selection, model training, cross-validation and deployment at scale.
- Experienced in data processing with Python, R & SQL.
- Defining data requirements to gather information and run statistical tests
- Research and development for data-driven analysis on structured and unstructured data sets
- Analyzing large, complex data sets, solving problems using advanced statistical and Machine Learning techniques
- Applied Machine Learning experience on LARGE datasets, with experience in regression, Bayesian Inference, decision trees, random forests, neural networks, feature selection, clustering etc.
- Use data science and machine learning techniques and best practices including processing, cleansing, and verifying the integrity of data used for analysis
- Work on Python/R/Scala in machine learning, deep learning, Natural Languag
- Processing and Text Analytics, Time series modelling
- Hands-on with ML frameworks like Keras, TensorFlow, Caffe, PyTorch, PySpark
- Should be enthusiastic to learn and use face recognition, image analytics, text analytics, language understanding, etc. from Azure to build cognitive applications
- Proven experience with machine learning tools like Scikit-learn, Numpy, SciPy, Pandas, Matplotlib, Jupyter Notebook
- Excellent problem-solving skills, innovative thinking, and ability to identify, propose and lead new research projects Able to develop tabular and multidimensional models that are compatible with warehouse standards
- Advanced knowledge in model evaluation, tuning and performance, operationalization and scalability of scientific techniques
- Prior experience working closely with teams in data warehousing, cleaning/transforming data for feature engineering, and leveraging visualization tools
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