Data Scientist

Ada Global

Posted: March 12, 2024 Openings: 10 Experience: 0-2 Salary: Preferred by Company
Education: Any Graduate Location: Bangalore
Job Type: Full Time Skills required: Python Typescript Communication Skills Power Bi Numpy Pandas Sql machine learning Data Analysis Data Science Artificial Intelligence

Job description

As a Data Scientist, you will be responsible for analyzing and interpreting complex datasets to provide valuable insights and solve challenging business problems
You will work closely with cross-functional teams to gather data, build predictive models, develop algorithms, and deliver data-driven solutions
This position is ideal for a fresher who possesses strong analytical skills, programming expertise, and a passion for working with data

Key Responsibilities:

Data Collection and Preprocessing:
- Collect, clean, and preprocess large volumes of structured and unstructured data from various sources.
- Conduct data quality assessments and implement data cleaning techniques to ensure accuracy and reliability.
Exploratory Data Analysis (EDA):
- Perform exploratory data analysis to understand the characteristics of the data and identify patterns, trends, and outliers.
- Utilize statistical techniques and visualizations to gain insights from the data.
Statistical Modeling and Machine Learning:
- Develop predictive models and algorithms using statistical techniques and machine learning algorithms.
- Apply regression analysis, classification, clustering, time series analysis, and other relevant methods to solve business problems.
- Evaluate model performance, fine-tune parameters, and optimize models for better accuracy.
Feature Engineering:
- Identify and engineer relevant features from raw data to enhance model performance.
- Conduct feature selection techniques to improve model interpretability and efficiency.
Model Deployment and Evaluation:
- Collaborate with software engineers to deploy models into production systems.
- Monitor model performance, diagnose issues, and implement improvements as needed.
Data Visualization and Reporting:
- Communicate findings and insights effectively through clear and concise data visualizations, reports, and presentations.
- Present complex data-driven concepts to non-technical stakeholders in a way that is easy to understand.
Continuous Learning and Research:
- Stay up-to-date with the latest advancements in data science, machine learning, and related fields.
- Conduct research and experimentation to explore new methodologies and approaches.