Senior Data Scientist - IT

SKILLS AND COMPETENCIES :

- Must Have Skills - Machine learning tools, Statistics. Any one language - (Python, Java, C/C++, R).

- Data Mining, Deep learning and deep learning toolkits like Tensorflow, Keras, and PyTorch etc. would be an added advantage.

- 12+ years of experience.

- Machine learning tools and techniques (e.g. k-nearest neighbours, random forests, ensemble methods, etc.)

- Statistics (e.g. hypothesis testing and summary statistics).

- Must have at least 2 or 3 projects successful projects (into production with clear impact) in ML + Business Insights areas.

- Knowledge of Python OR Java OR R OR SAS languages.

- SQL databases and database querying languages.

- Deep Learning and deep learning toolkits like Tensorflow, Keras, PyTorch etc.

- Software engineering skills (e.g. distributed computing, algorithms and data structures).

- Data mining, Cleansing, Visualization and reporting techniques.

- Awareness of Big data platforms like Hadoop, Hive & Pig, Spark, Scala.

- Prior experience in consumer segmentation, recommendation system is an added advantage.

- Prior experience in media and entertainment domain is an added advantage.

KEY RESPONSIBILITY AND ACCOUNTABILITY :

- You will help us discover the information hidden in vast amounts of data, and help make smarter decisions to deliver even better products.

- Conduct undirected research and frame open-ended industry questions.

- Focus will be in applying data mining techniques, doing statistical analysis, and building high-quality prediction systems integrated within various products.

- Responsibilities Understand and translate statistics and analytics to address client business problems.

- Employ sophisticated analytics programs, machine learning and statistical methods to prepare data for use in predictive and prescriptive modelling.

- Develop and implement analytical methodologies, processes, and technological solutions that integrate diverse information solutions and generate analytical insights. Invent new algorithms to solve problems and build new tools to automate work.

- Implement Deep Learning using deep learning toolkits.

- Communicate predictions and findings through effective data visualizations and reports.

- Work with team and mentor them for data science and advanced analytics.