e: Hybrid, work from anywhere with occasional office visits at Bangalore
Internship: Its for 6 months with 4.5LPA, which means it can be around 37,000 or plus or change in hand), then post internship salary range can varies based on performance post that….can be between 4.5LPA itself till 10.00 LPA purely can be on the basis of an outcome during your internship period.
Freshers are eligible till 2 year of exp.
The ideal candidate’s favorite words are learning, data, scale, and agility. You will leverage your strong collaboration skills and ability to extract valuable insights from highly complex data sets to ask the right questions and find the right answers. You are a data pro with deep statistical knowledge and analytical aptitude. You know how to make sense of massive amounts of data and gather deep insights. You will use statistics, data mining, machine learning, and deep learning techniques to deliver data-driven insights for clients. You are a thought leader with a commercial acumen, always on top of AI and ML trends.
Responsibilities
- Analyze raw data: assessing quality, cleansing, structuring for downstream processing
- Design accurate and scalable prediction algorithms
- Collaborate with engineering team to bring analytical prototypes to production
- Generate actionable insights for business improvements
Qualifications
- Freshers are eligible
- Deep understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms
- Fluency with Python, Database (MySQL/Postgres/MongoDB), Git versioning, Data Analysis Libraries (NumPy, Pandas, Statsmodels, Dask), Machine Learning Libraries( Scikit-learn), Deep Learning Libraries(TensorFlow/Keras/Pytorch/fast.ai), Data Visualization Libraries(Matplotlib, Plotly)
- Familiarity with Big Data frameworks and visualization tools (Cassandra, Hadoop, Spark, Tableau)
- Familiarity with AWS or Azure
Optional
· Team lead experience
· Consulting background
· clustering, auto-regression models, NER, topic modeling, object detection, semantics/instance segmentation, object tracking,
· Good understanding of Word2vec, RNNs, Transformers, Bert, Resnet, MobileNet, Unet, Mask-RCNN, Siamese Networks, GradCam, image augmentation techniques, GAN, Tensorboard
· Deployment – Flask, Tensorflow serving, Lambda functions, Docker