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
Written and verbal communication skills
Ability to work in a team
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.