Job Summary:
We are looking for a skilled Data Scientist (AI/ML) with 4-8 years of experience to develop and deploy machine learning solutions that solve complex business problems. The ideal candidate should have strong expertise in Artificial Intelligence, Machine Learning, Python, SQL, and Google Cloud Platform (GCP), along with experience in building predictive models and working with large-scale data.
You will collaborate with cross-functional teams to design, develop, and deploy AI-driven solutions while leveraging cloud technologies to build scalable and production-ready machine learning applications.
Key Responsibilities:
- Develop, train, and deploy machine learning and AI models to address business challenges.
- Collect, clean, preprocess, and analyze structured and unstructured datasets.
- Perform exploratory data analysis (EDA) and feature engineering to improve model performance.
- Build predictive, classification, clustering, and recommendation models using machine learning techniques.
- Develop scalable data pipelines and machine learning workflows.
- Optimize and fine-tune machine learning models for performance and accuracy.
- Deploy and monitor AI/ML models on Google Cloud Platform (GCP).
- Write efficient and optimized SQL queries to extract, transform, and analyze data.
- Collaborate with data engineers, software engineers, and business stakeholders to deliver end-to-end AI solutions.
- Create dashboards, reports, and visualizations to communicate analytical insights.
- Stay updated with the latest advancements in AI, machine learning, and cloud technologies.
Required Skills:
- 4-8 years of experience in Data Science, Artificial Intelligence, or Machine Learning.
- Strong proficiency in Python for machine learning and data analysis.
- Expertise in SQL for querying and managing large datasets.
- Hands-on experience with Google Cloud Platform (GCP) services for AI/ML and data engineering.
- Experience with machine learning libraries such as Scikit-learn, TensorFlow, PyTorch, or XGBoost.
- Strong understanding of statistical modeling, predictive analytics, and model evaluation techniques.
- Experience with data preprocessing, feature engineering, and model deployment.
- Knowledge of version control tools such as Git.
- Strong analytical, problem-solving, and communication skills.
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