Data Scientist - Machine Learning
First seen by Alion on Oct 27, 2026.
Experience:
3 - 7 years of relevant ML experience.
Education:
B. Tech from a Tier-1 college preferred; MS/M. Tech in CS, IT, or a related field is an advantage.
Key Technical Skills:
Programming:
- Strong proficiency in at least one of Python, R, or Java; Python is highly relevant.
Machine Learning & Statistics:
- Strong understanding of ML algorithms, statistical concepts, hypothesis testing, and predictive/prescriptive modeling.
Python Libraries:
- NumPy, Pandas, SciPy, Scikit-learn, IPython.
Big Data & Cloud:
- PySpark, AWS, Linux, Jupyter.
Data Analysis:
- EDA, data extraction, data transformation, data preparation, and interpretation.
Key Responsibilities:
- Develop and validate statistical and machine learning models.
- Perform hypothesis testing, root cause analysis, factor analysis, and insight generation.
- Train and test models, improve accuracy, and monitor performance.
- Extract and transform data from enterprise data warehouses and big data platforms.
- Solve complex, unstructured business problems and engage with clients to understand business requirements.
Required Soft Skills:
- Strong problem-solving and analytical abilities.
- High ownership and willingness to learn.
- Team management capabilities.
- Comfortable working in a fast-paced, deadline-driven environment.
- Passion for technology and client-facing problem-solving.
Ideal Candidate Profile:
- A Data Scientist / ML Engineer with 3 - 5 years of hands-on experience developing and validating ML models, strong statistical knowledge, proficiency in Python or another programming language, and practical exposure to data preparation, EDA, and big data technologies such as PySpark and AWS.
Skills:
- python, machine learning, aws
Skills
Data Science, Analytics, Machine Learning, Python, Statistics, Data Modeling, Data Visualization, SQL, Data Analytics