Applied Scientist

Applied Scientist Intern
Location: Bangalore
Employment Type: Internship
Full-Time Opportunity: Based on performance
About the Role
We are looking for a highly motivated, research-oriented, and analytical Applied Scientist Intern to join our team. This role sits at the intersection of practical Machine Learning, advanced statistical modeling, and strategy formulation. As an Applied Scientist Intern, you will focus on taking theoretical concepts, mathematical frameworks, and algorithms and applying them to real-world datasets to build scalable models and data-driven solutions.
The ideal candidate possesses strong mathematical rigor, deep technical curiosity, a passion for experimentation, and a problem-solving mindset capable of translating complex data into actionable insights.
Key Responsibilities
Formulate, design, and implement machine learning, statistical, and algorithmic models to address complex analytical challenges.
Preprocess, structure, and feature-engineer large-scale datasets for experimental and production pipelines.
Conduct rigorous hypothesis testing, quantitative research, and exploratory data analysis to discover predictive signals and patterns.
Systematically evaluate, benchmark, and fine-tune model performance using standard validation metrics and backtesting techniques.
Bridge the gap between research and practical execution by developing clean, reproducible, and efficient code.
Identify data anomalies, systemic biases, and model failure modes, proposing scientific solutions to resolve them.
Document experimental methodologies, mathematical derivations, model architectures, and results clearly for cross-functional review.
Collaborate closely with AI/ML engineers, data scientists, and research teams to advance ongoing strategic initiatives.
Requirements
Education: B.Tech/B.E., M.Tech, or Integrated M.Sc. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Statistics, Quantitative Finance, or a related field.
Graduation Year: 2026 pass-out candidates only.
Strong mathematical foundation in Linear Algebra, Probability, Statistics, and Calculus.
Solid understanding of core Machine Learning algorithms (Supervised/Unsupervised, Optimization Techniques, Loss Functions).
Proficiency in Python and fundamental data science libraries (NumPy, Pandas, SciPy, Scikit-learn).
Familiarity with deep learning or advanced scientific frameworks (PyTorch, TensorFlow, etc.) is a strong plus.
Demonstrated ability to approach ambiguous, unstructured problems with structured scientific rigor.
Excellent logical reasoning and analytical problem-solving skills.
Good to Have
Exposure to Quantitative Research, Algorithmic Modeling, Signal Processing, or Financial Markets.
Prior research experience, publication effort, or hands-on projects involving model formulation, statistical inference, or optimization.
Familiarity with software engineering best practices (Git, clean code principles, modular development).
Eligibility
B.Tech/B.E./M.Tech – 2026 batch graduates.
Candidates from Computer Science, AI/ML, Data Science, Applied Mathematics, Statistics, Engineering, or related quantitative disciplines.
Candidates with exceptional quantitative, mathematical, and algorithmic capabilities are strongly encouraged to apply.

Work arrangement
No