Data Science - Associate Software Engineer | Software Engineer

Devsinc is seeking motivated Data Scientists with 1-3 years of experience , particularly in Artificial Intelligence (AI) or Machine Learning (ML) . This role is ideal for individuals who have built a strong foundation in ML methodologies and are eager to apply their skills to real-world business challenges. You will work closely with cross-functional teams to design, deploy, and optimize ML-driven solutions that support data-driven decision-making and innovation.

Key Responsibilities:

Model Development: Design, develop, and deploy ML models for business use cases, including data preprocessing, feature engineering, model training, evaluation, and deployment.

Data Exploration: Conduct exploratory data analysis (EDA) to uncover patterns, correlations, and insights that inform model refinement and business strategies.

Collaboration: Work with data scientists, engineers, and business stakeholders to translate business needs into ML-driven solutions.

Visualization & Reporting: Build clear, compelling visualizations and reports to communicate ML outcomes and insights to both technical and non-technical audiences.

Continuous Learning: Stay updated with the latest advancements in ML algorithms, tools, and best practices, and incorporate them into projects where applicable.

Prototyping: Develop and test prototypes for predictive and analytical models in real-world scenarios.

Communication: Maintain clear, structured communication to articulate data needs, methodologies, and outcomes effectively.

Cross-Functional Impact: Identify opportunities to reuse datasets, code, or models across multiple business areas.

Requirements

Education: Bachelor's degree in Data Science, Computer Science, Engineering, Mathematics, Statistics, or a related field (with significant ML coursework/projects).

Experience: 1-3 years of experience of hands-on experience in ML or data science, supported by a portfolio of relevant projects (model development, feature engineering, data analysis).

Technical Skills:

• Proficiency in Python and ML frameworks/libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Experience with SQL for data manipulation.
• Familiarity with data visualization tools/libraries (e.g., Matplotlib, Seaborn, ggplot2).
  
  

Analytical Skills: Strong ability to analyze large and complex datasets and derive meaningful insights.

Communication: Ability to explain technical concepts and ML results to non-technical stakeholders.

Teamwork: Demonstrated ability to work collaboratively, adapt to feedback, and contribute effectively in a team environment.