Data Scientist

Job Specification: Data Scientist

Work Location: Bangalore

Experience & Skills

• 3 to 5 yrs. of experience with Python as a Data Scientist
• Degree in Engineering, Science, or a similar Quantitative field, preferably with a related postgraduate degree or Ph.D.
• Exploratory data analysis skills.
• Knowledge and experience with machine learning algorithms (supervised and unsupervised).
• Knowledge of statistical techniques to support exploratory findings and conclusions
• Strong experience in one of the programming languages used in data science (Python, R, MATLAB or similar).
• Experience with some of the related packages Numpy, Pandas, Matplotlib, scikit-learn, TensorFlow, etc.
• Experience with Machine learning / Deep learning, such as Deep Neural Networks, Tensorflow, Caffe, Keras, PyTorch.
• Experience with artificial intelligence techniques: image recognition, NLP, text mining, chatbots.
• Experience in deploying machine and deep learning solutions.
• Experience in the design of analytical models: (Decision Trees, Scoring, Clustering, pattern discovery, trends, regressions, Deep learning...).
• Experience of Agile and traditional SDLC delivery methodologies.
• Working experience on Azure Databricks, Data lake and Datafactory is good to have skillset
• Outstanding analytical and problem-solving skills
• A methodical and logical approach
• The ability to plan work and meet deadlines
• Accuracy and attention to detail
• Interpersonal skills
• Written and verbal communication skills
• Ability to work in a team
  
  

Job Responsibilities

As a Data scientist engineer, he/she has to work on different dataset and apply the different ML with correct pre-processing steps based on the understanding of dataset and able to analyse and mention the ML model which suits for this dataset , when we pass the live data once pushed to production

• Have to work in building statistical Machine Learning models such as regression, classification and clustering models.
• Also, have to work on Deep Learning and NLP techniques.
• Processing, cleansing & verifying of data, updating missing data, organising data in to usable formats
• Analysing data for trends and patterns and to find answers to specific questions
• Preparing reports for executive and project teams
• Create visualisations of data through different techniques.
• Identifying relevant data sources for business needs. Collecting structured and unstructured data.
• Working on the Azure MLOps setup for the model development and deployment to the production.