A Data Scientist is expected to design the pipeline right from data ingestion to storage to curation to building models considering the business requirements. A DS should give importance to extract the quality data, experiment and choose the best-fit machine learning model and plan for enhancements based on A/B testing or other means. A DS should be abreast with the new opensource tools that get built and evaluating the relevance to our projects. They should be creating the storyline for presenting any insights that are derived from the bigdata, trivial or non-trivial.

Responsibilities:

1.     Understand the business requirements, ideate and advise an experimental solution.

2.     Elicit the architecture for the big data processing of various data sources.

3.     Accentuate the anomaly removal in bigdata and propose solutions that handle both structured and unstructured data.

4.     Builds recommendation engines for various use-cases based on data like location, media consumption etc. of users.

5.     Build machine learning/statistical models to give the results that help sales and marketing teams.

6.     Extract meaningful insights by experimenting with relevant ML models.

7.     Help in resolving the team’s technical queries, blockers etc.

8.   Suggest futuristic bigdata pipelines and algorithms to best fit the growing bigdata.

Skills:

1. LSTM, Machine Learning, Regression etc, Clustering algorithm, Classification problems.
2. Knowledge in ETL and big data technologies like Hadoop, spark
3. Must have deployed Machine learning solutions in production environment
4. Must be good in Data structures and Algorithms
5. Strong in python coding 
6. Strong in PyTorch or TensorFlow or Keras
7. Knowledge in Flask, Spark ML, Queuing System like Kafka, Databases.