Manager - Data Science
1 month ago
About the job :
We are hiring for Data Science/Data Analytics role for tier1 Management consulting org,
Job location : are we looking for :
Years of Experience : 5+ years
Education : MBA
Add eligibility : Banking and Insurance/Energy and Utilities
Industry domain knowledge
Add about desired experience : 7+ years
Tools & Techniques :
- Data Strategy, Data Architecture and Solution design Knowledge.
- Industry and functional consultation experience.
- Expertise in providing proposals and business architectures, presentations.
- Should have participated in multiple AI based RFP submission.
- Knowledge of Cloud Azure/AWS/Google.
- Knowledge of Python programming preferred.
- Strong domain knowledge of either Energy and Utilities Industry or Banking and Insurance Industry.
- Implementation knowledge of GenAI based solutions will be added advantage.
Roles & Responsibilities :
- Data Acquisition and Wrangling : Collect data from various sources, clean and transform it into a usable format for analysis.
- Exploratory Data Analysis (EDA): Perform exploratory data analysis to understand data distribution, identify patterns, and uncover potential relationships between variables.
- Statistical Modeling: Develop and apply statistical models to analyze data, test hypotheses, and make predictions.
- Machine Learning Model Development: Build, train, and evaluate machine learning models for tasks like classification, regression, or clustering, depending on the project goals.
- Data Visualization: Create clear and informative visualizations to communicate findings to both technical and non-technical audiences.
- Communication and Collaboration: Effectively communicate complex data science concepts to various stakeholders, including business analysts, developers, and executives.
- Stay Up-to-Date: Continuously learn and adapt to new technologies and advancements in the data science field
- Cloud Computing: Experience with cloud platforms (AWS, Azure, GCP) is increasingly desired as data storage and processing often leverage cloud resources.
- Big Data Processing: Familiarity with big data frameworks (Hadoop, Spark) can be valuable for handling massive datasets.
- Data Version Control: Utilizing Git for version control of code and data analysis notebooks is becoming increasingly important.
- Domain Knowledge: In some industries, having specific domain knowledge (e.g., finance, healthcare) can be a significant advantage.
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