Description :

We are looking for a Data Scientist / AIML Engineer with 3+ years of experience to build and deliver end-to-end data science and AI solutions. The role involves applying statistical thinking, machine learning, deep learning, and Generative AI to solve real-world business problems, working closely with cross-functional teams to translate data into actionable insights and production-ready models.

Duties and responsibilities :

- 3+ years of relevant experience in Data Science / AIML

- Experience with end-to-end data science projects, from problem formulation to model evaluation and deployment

- Hands-on exposure to production-grade AI/ML solutions

- No upper limit on total work experience

Skills & competencies :

- Machine Learning (supervised & unsupervised learning)

- Python (hands-on coding, data handling, writing functions and classes)

- SQL (data extraction, joins, aggregations)

- Statistics (sampling, distributions, hypothesis testing, regression, MLE)

- Deep Learning (neural networks, model optimization)

- Generative AI (preferred) : LLMs, RAG, prompt engineering, basic fine-tuning

Minimum education :

- Strong educational background in Mathematics or Statistics (preferred).

Technical Competencies :

Machine Learning :

- Classification, regression, clustering

- Algorithms : Boosting, k-NN, Naive Bayes, SVM, Decision Trees, Random Forests

Data Analysis & Feature Engineering :

- Exploratory Data Analysis (EDA)

- Feature engineering and data preprocessing

Data Visualization :

- Matplotlib, ggplot

- Power BI or Tableau (good to have)

Key Responsibilities :

- Perform exploratory data analysis to identify patterns, trends, and insights

- Build, validate, and optimize statistical and ML models

- Translate business problems into analytical and ML-driven solutions

- Develop end-to-end data science workflows, from problem definition to model evaluation

- Communicate findings through clear narratives and effective visualizations

Good to Have :

- Power BI / Tableau, Streamlit for dashboards and demos

- Exposure to RCM (Revenue Cycle Management) domain

- Experience handling large datasets or big data frameworks

- Knowledge of model deployment, MLOps, or cloud platforms

- Strong mathematical foundation in statistics or probability