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Data Scientist
Job Description
About the Role We are looking for an experienced Data Scientist to lead the design and development of data-driven solutions that enable smarter business decisions and predictive capabilities. The ideal candidate combines strong analytical skills, statistical expertise, and hands-on experience with machine learning and big data technologies. You will work collaboratively with data engineers, analysts, and business stakeholders to deliver insights and scalable ML models that create measurable impact.
Key Responsibilities Data Exploration & Analysis
Collect, clean, and analyze structured and unstructured data from multiple sources to uncover meaningful insights and trends. Model Development
Design, build, and deploy machine learning and statistical models to solve business problems such as forecasting, classification, recommendation, and optimization. Feature Engineering
Identify, create, and select the most relevant variables and features to improve model performance and interpretability. Experimentation & Validation
Apply hypothesis testing, A/B testing, and cross-validation techniques to evaluate model robustness and performance. Production Deployment
Work with data engineering and MLOps teams to operationalize models, monitor performance, and ensure scalability and reliability in production environments. Visualization & Storytelling
Communicate complex analytical findings in clear, concise, and visually compelling ways for both technical and non-technical audiences. Collaboration
Partner with business teams to understand objectives, define success metrics, and translate business requirements into analytical frameworks. Continuous Improvement
Stay current with advances in machine learning, AI, and data science technologies, incorporating them into projects and best practices.
Technical Expertise
Strong proficiency in Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow) or R. Expertise in machine learning algorithms (supervised, unsupervised, NLP, and deep learning). Strong understanding of statistical modeling, probability, and mathematical optimization. Experience with SQL and data manipulation in large datasets. Familiarity with big data platforms (e.g., Spark, Databricks, Hadoop) and cloud environments (AWS, Azure, or GCP). Exposure to MLOps tools (MLflow, Kubeflow, Airflow, Docker, CI/CD). Experience with data visualization tools (Power BI, Tableau, Matplotlib, Seaborn, Plotly).10-15 yrs experience
Key Responsibilities Data Exploration & Analysis
Collect, clean, and analyze structured and unstructured data from multiple sources to uncover meaningful insights and trends. Model Development
Design, build, and deploy machine learning and statistical models to solve business problems such as forecasting, classification, recommendation, and optimization. Feature Engineering
Identify, create, and select the most relevant variables and features to improve model performance and interpretability. Experimentation & Validation
Apply hypothesis testing, A/B testing, and cross-validation techniques to evaluate model robustness and performance. Production Deployment
Work with data engineering and MLOps teams to operationalize models, monitor performance, and ensure scalability and reliability in production environments. Visualization & Storytelling
Communicate complex analytical findings in clear, concise, and visually compelling ways for both technical and non-technical audiences. Collaboration
Partner with business teams to understand objectives, define success metrics, and translate business requirements into analytical frameworks. Continuous Improvement
Stay current with advances in machine learning, AI, and data science technologies, incorporating them into projects and best practices.
Technical Expertise
Strong proficiency in Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow) or R. Expertise in machine learning algorithms (supervised, unsupervised, NLP, and deep learning). Strong understanding of statistical modeling, probability, and mathematical optimization. Experience with SQL and data manipulation in large datasets. Familiarity with big data platforms (e.g., Spark, Databricks, Hadoop) and cloud environments (AWS, Azure, or GCP). Exposure to MLOps tools (MLflow, Kubeflow, Airflow, Docker, CI/CD). Experience with data visualization tools (Power BI, Tableau, Matplotlib, Seaborn, Plotly).10-15 yrs experience
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Key Skills
About Company
Skills:
data preparation , Sql, Python, Machine Learning, Agile Development, Data Manipulation, Natural Language Processing, Clustering, Statistical Analysis, ML Ops, Azure AI Search, anomaly detection, UNIX Command Line, Classification, Model Evaluation
Skills:
Tensorflow, Nlp, MLops, Pytorch, Spark, Databricks, Python, Computer Vision, GenAI, Llm, AWS Sagemaker, Vertex AI, Azure AI, DL frameworks
Skills:
Python, Sql, Hadoop, Spark, Data Warehousing, Etl, Machine Learning, Data Modeling, Cloud Computing, Api Development
Skills:
Machine Learning, Clustering, Sql, Deep Learning, Neural Networks, Predictive Modeling, Python, Decision Trees, Ai, Ensemble methods, Model training, Feature engineering, Data ingestion, Responsible AI, Explainable AI
Skills:
Pyspark, Sql, Nlp, Pytorch, Databricks, Python, MLops, LangChain, Generative AI, MLflow, LangGraph, Agentic AI, LLM APIs, Azure AI services, LLMs, AI governance frameworks, Medical text analytics, Knowledge graphs, clinical decision support, Multimodal AI, CI/CD for ML