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AI/ML Engineer - SSE/LSE
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Pune, Maharashtra, India
Department
AI/ML
Job posted on
Nov 14, 2025
Employment type
Full Time Employee
Key Responsibilities:
Data Collection & Preparation:
Assist in gathering, extracting, and integrating data from various sources (e.g., databases, APIs, external files).
Perform data cleaning, preprocessing, and wrangling to ensure data quality, consistency, and readiness for analysis.
Identify and address data inconsistencies, missing values, and outliers.
Exploratory Data Analysis (EDA):
Conduct exploratory data analysis to understand data structures, identify patterns, trends, and relationships.
Generate descriptive statistics and create visualizations to communicate initial findings.
Model Development & Implementation (Supportive Role):
Assist in the development, testing, and evaluation of statistical models and basic machine learning algorithms (e.g., regression, classification).
Support the feature engineering process, transforming raw data into features for model training.
Help with model validation and performance monitoring.
Insights & Reporting:
Translate analytical findings into clear, concise, and actionable insights for both technical and non-technical stakeholders.
Create data visualizations, dashboards, and reports using tools like Tableau, Power BI, or Matplotlib/Seaborn to effectively present results.
Collaboration & Learning:
Work closely with senior data scientists, data engineers, business analysts, and other cross-functional teams to understand business problems and deliver data-driven solutions.
Actively participate in team meetings, discussions, and knowledge-sharing sessions.
Continuously learn and stay updated with the latest data science techniques, tools, and best practices.
Required Skills & Qualifications:
Education: Bachelor's or Master's degree in a quantitative field such as Data Science, Statistics, Computer Science, Mathematics, Engineering, or a related discipline.
Programming: Proficiency in at least one programming language commonly used in data science (e.g., Python, R).
Data Manipulation: Experience with data manipulation libraries (e.g., Pandas, NumPy in Python).
Database Skills: Solid understanding of SQL for querying and managing databases.
Statistical Foundation: Basic understanding of statistical concepts, hypothesis testing, and probability.
Machine Learning Fundamentals: Familiarity with core machine learning concepts and common algorithms.
Problem-Solving: Strong analytical and problem-solving skills with attention to detail.
Communication: Excellent written and verbal communication skills, with the ability to explain technical concepts to non-technical audiences.
Teamwork: Ability to work effectively in a collaborative team environment.
Preferred (Nice-to-Have) Skills:
Experience with data visualization tools (e.g., Tableau, Power BI).
Familiarity with cloud platforms (e.g., AWS, Azure, GCP) or big data technologies (e.g., Spark, Hadoop).
Experience with version control systems (e.g., Git).
Completed relevant data science projects (academic or personal portfolio).
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