Description

You will design and implement complex machine learning solutions across various domains. You own the end-to-end modeling workflow, from initial data exploration to final deployment.

Responsibilities

• Design ML solutions by interacting with stakeholders and internal IT teams.
• Execute end-to-end modeling workflows including EDA, hypothesis testing, feature engineering, and hyperparameter tuning.
• Apply deep learning, computer vision, and NLP techniques to solve classification, detection, and sequence modeling tasks.
• Develop forecasting models for univariate and multivariate data using statistical and neural network approaches.
• Manage multiple engagements simultaneously within an agile environment.

Required Skills

• 7-12 years of experience in machine learning and analytical problem solving.
• Proficiency in Python or R with Scikit-Learn, Pandas, Matplotlib, Seaborn, TensorFlow, PyTorch, and Keras.
• Deep expertise in ML algorithms: Regression, Classification (Decision-trees, Random Forests, SVM, ANNs), Clustering (k-means, DBSCAN), and Ensemble techniques (XGBoost, CatBoost, LightGBM).
• Experience with Computer Vision: CNNs, Faster R-CNN, Mask R-CNN, YOLO, SSD, Detectron2, and OpenCV.
• Strong NLP background: Transformers, BERT, GPT, RNNs, LSTMs, TF-IDF, and NER.
• Expertise in dimensionality reduction (PCA, SVD) and forecasting (ARIMA, SARIMA, Exponential Smoothing).
• Proven ability to communicate technical findings to both internal and external stakeholders.
• Strong quantitative skills for error analysis and cross-validation.

Preferred Skills

• Experience with cloud platforms (GCP, Azure, AWS) and visualization tools like Tableau, Power BI, or Looker Studio.
• Ability to build APIs using Flask, Django, or FastAPI.

Key Skills

Education

Any Graduate