About the Role
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.
This opening is for an Entry-Level position. You are required to first go through the following:
- On the Job training: You will be paid a stipend during this period (during those 12 weeks).
- Certification: For Machine Learning Engineer.
- Paid internship: For the next three months.
- Followed by your permanent placement.
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.
- Strong ability to communicate deep analytical results in forms that resonate with scientific and/or business collaborators, highlighting actionable insights.
- Entrepreneurial inclination to discover novel opportunities for applying analytical techniques to business/scientific problems across the company.
Qualifications & Skills
- BTECH/MTECH/BCA/MCA/BSC Maths, Statistics or any other equivalent degree with excellent programming skills.
- Strong knowledge of Programming & associated skills.
- Strong hold in Data Structures and Algorithm.
- Strong discrete math, computational complexity.
- Familiarity with Languages - Python (primary), Sklearn/Pandas/Numpy.
- Deep Learning (Tensorflow, Keras etc.).
- NLP (StanfordNLP, Open NLP, Gensim, Spacy).
- Experience in Data wrangling/Feature Engineering.
- Experience in machine learning algorithm development.
- Prediction models like Client churn prediction.
- Excellent familiarity with ML techniques (standard, deep learning etc).
- Must have experience in using Statistical techniques like Regression Modeling/ Optimization/ Structuring/ Machine Learning Techniques.
- PhD/Master's Degree in Statistics, Mathematics, Computer Science, or equivalent is preferred.
- Under-the-hood knowledge of many of these machine learning concepts: supervised/unsupervised learning, loss functions, regularization, feature selection, regression/classification, cross-validation, bagging, kernel methods, sampling, probability distributions.
- Strong knowledge of Computer vision techniques like OpenCv.
- Familiarity with Dgango Restapi framework.
- At least 1 - 2 years' of experience in quantitative analytics or data modeling (preferred).
- Deep understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms.
- Fluency in a programming language (Python, C, C++, Java, SQL).
- Familiarity with Big Data frameworks and visualization tools (Cassandra, Hadoop, Spark, Tableau).