Job Description – Data Scientist

3-5 years experience

Roles & Responsibilities:

1. Ability to understand a problem statement and implement solutions & techniques for solving natural language processing, text analytics, and information extraction problems, as well as structured data problems.
2. Work and collaborate with other teams to deliver and create value for clients
3. Fast learner: ability to learn and pick up a new language/tool/ platform quickly
4. Conceptualize, design and deliver high-quality solutions and insightful analysis
5. Conduct research and prototyping innovations; data and requirements gathering; solution scoping and architecture; consulting clients and client facing teams on advanced statistical and machine learning problems.
6. Come up with actionable ideas to solve problems and implement those ideas.
7. Communicate context, data, solution and implications to the team, senior leaders and stakeholders.

Skills:

1. Intermediate to expert level proficiency in at least one of Python and R
2. Ability to discover effective solutions to complex problems. Strong skills in data-structures and algorithms.
3. Experience of working on a project end-to-end: problem scoping, data gathering, EDA, modeling, insights, and visualizations
4. Problem-solving: Ability to break the problem into small problems and think of relevant techniques which can be explored & used to cater to those
5. Intermediate to advanced knowledge of regular expressions, machine learning, probability theory, information theory, statistics, and algorithms. Discuss and use various algorithms and approaches on a daily basis.
6. Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, MySQL, etc.

Qualifications & Experience :

1. Bachelor’s or Master’s degree in engineering.
2. Good knowledge of Basic Statistics (Hypothesis testing, probability, distributions, etc.)
3. Exposure towards multivariate statistical Analysis (such as PCA, PLS, etc.).
4. Strong in Machine learning and supervised Learning techniques such as ANN, Decision Trees, SVM, Naïve Bayes etc.
5. Knowledge on Unsupervised learning techniques such as k-means, hierarchical clustering etc.