What are the responsibilities and job description for the Data Science Intern position at SMC³?
Position Summary
We are seeking a motivated and curious Data Science Intern to join our growing data science team. This internship is designed for individuals with foundational experience (1–3 years through coursework, projects, or prior roles) who are eager to apply data science and machine learning techniques to real-world business challenges.
In this role, you’ll help develop, optimize, and deploy machine learning models and data-driven solutions while learning modern ML Ops best practices. You’ll collaborate closely with experienced data scientists, engineers, and product partners, gaining hands-on exposure to production-ready data science in a supportive, fast-paced environment.
If you’re passionate about using data, machine learning, and AI to solve meaningful problems—and want to continue building your skills alongside a collaborative team—this internship is for you.
Key Responsibilities
We are seeking a motivated and curious Data Science Intern to join our growing data science team. This internship is designed for individuals with foundational experience (1–3 years through coursework, projects, or prior roles) who are eager to apply data science and machine learning techniques to real-world business challenges.
In this role, you’ll help develop, optimize, and deploy machine learning models and data-driven solutions while learning modern ML Ops best practices. You’ll collaborate closely with experienced data scientists, engineers, and product partners, gaining hands-on exposure to production-ready data science in a supportive, fast-paced environment.
If you’re passionate about using data, machine learning, and AI to solve meaningful problems—and want to continue building your skills alongside a collaborative team—this internship is for you.
Key Responsibilities
- Assist in the development and optimization of machine learning models, including large language models (LLMs), neural networks, tree-based algorithms, and statistical methods.
- Analyze structured, semi-structured, and unstructured datasets, applying feature engineering and statistical techniques to extract actionable insights.
- Help design and maintain end-to-end machine learning pipelines, focusing on scalability, reproducibility, and efficient retraining.
- Prototype and support the development of domain-specific AI agents for tasks such as information retrieval, data extraction, and intelligent automation.
- Perform data exploration, preprocessing, and visualization to identify trends and clearly communicate findings.
- Collaborate with data engineers, software developers, and product owners to integrate machine learning solutions into business applications and cloud platforms.
- Research and experiment with emerging algorithms, frameworks, and tools to improve model performance, efficiency, and scalability.
- Assist with maintaining and improving existing models and analytics solutions as business needs evolve.
- Contribute to team success through documentation, code reviews, and collaboration, supporting a strong data-driven culture.
- Currently pursuing or recently completed a Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
- 1–3 years of applied experience in data science or machine learning through internships, academic research, coursework, or professional roles.
- Proficiency in Python, with experience using libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, and HuggingFace .
- Experience with R, JavaScript, Java, or similar languages is a plus.
- Hands-on experience with supervised and unsupervised learning techniques, including regression, classification, clustering, decision trees, and neural networks.
- Understanding of feature engineering, hyperparameter tuning, and model evaluation metrics.
- Exposure to LLM concepts, such as prompt engineering, fine-tuning, and context retrieval, is a strong plus.
- Experience cleaning, transforming, and analyzing large datasets to generate meaningful insights.
- Exposure to cloud-based tools and platforms for data science and machine learning (e.g., Azure ML, MLflow, or similar).
- Ability to clearly communicate technical concepts to both technical and non-technical audiences.
- Self-directed with a strong desire to learn
- Analytical and creative problem-solving skills
- Strong interpersonal and collaboration skills
- Clear written and verbal communication
- Attention to detail and accuracy
- Adaptability in a fast-paced environment
- Reliable and accountable team contributor