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.