PwC - Data Science Engineer - R/Python (4-8 yrs)

Job Description :

Relevant experience : 5+ years of experience in Statistical and Machine learning models.

- Proficient in Python for machine learning.

- Proficient in Data Visualization in python / R or using BI Tools such as PowerBI/Tableau/Qlik

- Proficient in Statistical modelling techniques and Machine Learning Algorithms

- Proficient in knowledge of cloud Architecture, predominantly MS AZURE.

- Proficiency in Optimization techniques with open source tools is a plus

- Proficient in CI/CD processes in product deployment and used it in delivery. Should have understanding of Dockerization, REST APIs and MLOps as whole.

- Working knowledge on following agile practices in product development is a plus

- Experience in various statistical and machine learning models, data mining, and unstructured data analytics in corporate or academic research environments

- Proven background in at least one of the following - Multivariate time series forecasting, Reliability models, Markov Models, Stochastic models, Bayesian Modelling, Classification Models, Cluster Analysis, Neural Network, Non-parametric Methods, Multivariate Statistics

- Ability to translate domain problems to data science problem

- Ability to learn new technologies continuously and go to implementation

- Ability to think creatively to solve real world business problems

- Ability to work in a global collaborative team environment

- Proficient verbal and written communication skills in English

Roles and Responsibilities :

- Design and implement advanced statistical and machine learning models.

- Utilize Python for machine learning tasks and solutions on MS Azure.

- Create data visualizations using Python, R, PowerBI, Tableau, or Qlik.

- Apply and optimize machine learning algorithms for performance.

- Implement CI/CD pipelines, Docker, REST APIs, and MLOps processes.

- Collaborate with cross-functional teams using agile methodologies.

- Develop models including time series forecasting, Markov models, Bayesian analysis, and neural networks.

- Translate business problems into data science solutions.

- Continuously learn and apply new technologies and techniques.

- Collaborate effectively with global teams.

- Communicate technical results to both technical and non-technical stakeholders.