What you should have?

• Minimum 8 years of relevant experience in data analytics.


• Experience in leading and executing multiple data science projects in the past Client handling experience Qualification in statistics or mathematics.


• Any data science certifications good understanding on ML & DL algorithms and frameworks (Scikit-learn, NLTK, Gensim, OpenCV, Tensorflow/Keras/PyTorch/H2Oetc.)


• Should have worked on diverse projects related to predictive analytics (supervised & unsupervised), prescriptive analytics (optimization).


• Machine Learning, Deep Learning, NLP and computer vision.


• Well versed with data science methodologies and deployment frameworks.


• Good proficiency on Python/R/Tableau/Power BI Understanding of RDBMS concepts - SQL, Oracle.


• Understanding and experience of data analytics solution delivery (end to end).


• Strong business acumen Willing to work in a challenging environment.

Desirable Skills :

• Big data & data engineering skils (Hadoop, Kafka, ELK, Spark, Splunk etc.).


• Working experience on Machine learning DevOps (deploying production workflows).


• Experience on web frameworks like R-Shiny, Flask or Django Experience with Docker, Dask, Airflow and MLflow.


• Experience in productionizing Machine Learning models in the cloud (Azure, GCP or AWS).


• Experience on cloud ML services like Azure - ML Services, AWS -Sagemaker, Google.


• ML Domain experience in manufacturing industry Understanding of No-SQL databases - MongoDB, Cassandra, Neo4j.

What you will do?

• Push the frontiers of what is possible in the area of machine learning to create new Incubation.


• Explore, understand, and implement the most recent algorithms and approaches for supervised and unsupervised machine learning and deep learning.


• Comfortably handle multi-terabyte data sets in scale-up and scale-out environments.


• Understand business processes that create and consume data to be able to select the best approaches, evaluate their performance, and assess business relevance.


• Create excellence both in terms of results quality and system scalability through continuous evaluation, analysis, and refinement of the system implementation.


• Communicating the relevance of implemented systems and achieved results in a visual and consistent way.


• Work closely with the team (product owner, designers, and other developers) and customers to holistically understand business and user requirements and derive adequate application development concepts.


• Performing quality assurance tasks.


• Working together with partners and customers Solve complex integration issues.


• Ability to work in global teams with different time zones.


• Immerse yourself quickly in new topics, terminology, and development tasks.