Embedded Technosolutions is looking for Data Science Developer & Trainer to join our dynamic team and embark on a rewarding career journey We are seeking a skilled and motivated Data Science Developer to join our dynamic team.
The ideal candidate will have a strong background in data science, machine learning, and software development.
As a Data Science Developer, you will be responsible for designing, developing, and implementing data-driven solutions that contribute to the success of our organization.
You will work closely with cross-functional teams to understand business requirements and translate them into actionable insights and scalable data solutions.
Key Responsibilities:Data Analysis and Exploration:Utilize statistical methods and machine learning techniques to analyze large datasets.
Perform exploratory data analysis to uncover trends, patterns, and insights.
Collaborate with business stakeholders to understand data requirements and objectives.
Algorithm Development:Design, develop, and implement machine learning algorithms and models.
Fine-tune and optimize models for performance and scalability.
Stay current with industry trends and advancements in data science and machine learning.
Programming and Software Development:Develop software applications and tools to support data science initiatives.
Use programming languages such as Python, R, or Java to build scalable and efficient solutions.
Collaborate with software engineers to integrate data science models into production systems.
Data Preprocessing and Cleaning:Clean and preprocess raw data to prepare it for analysis and modeling.
Handle missing data and outliers effectively.
Ensure data quality and integrity throughout the data science pipeline.
Model Deployment and Integration:Deploy machine learning models to production environments.
Collaborate with IT and DevOps teams to integrate models into existing systems.
Monitor and maintain deployed models, ensuring ongoing accuracy and performance.
Documentation and Communication:Document code, algorithms, and methodologies for knowledge sharing.
Communicate findings and insights to both technical and non-technical stakeholders.