Data Analyst​/Scientist

Responsibilities

• Develop, train and optimize machine learning models for classification, regression, forecasting, clustering, NLP and deep learning use cases.
• Build AI‑driven solutions such as recommendation engines, anomaly detection systems, predictive scoring models and generative AI applications.
• Perform end‑to‑end data science workflows including data acquisition, cleaning, feature engineering, model selection, validation and deployment.
• Collaborate with data engineers to design scalable data pipelines and ensure high‑quality data availability for modelling.
• Implement MLOps practices such as model versioning, monitoring, retraining and performance tracking.
• Conduct statistical analysis, experiment design and A/B testing to validate model effectiveness and business impact.
• Work with cloud platforms (Azure, AWS or GCP) to train, deploy and operationalize ML models.
• Communicate insights, model results and recommendations to technical and non‑technical stakeholders through dashboards, reports and presentations.
• Integrate models into business applications using APIs, cloud services or automation frameworks.
• Stay updated on emerging AI & ML technologies, LLMs and best practices to continuously improve modeling approaches.

• Strong proficiency in Python and ML libraries such as Pandas, Num Py, Scikit‑learn, Tensor Flow, or PyTorch.
• Solid understanding of machine learning algorithms, statistical modeling, and model evaluation techniques.
• Experience with SQL and working with relational databases.
• Familiarity with cloud ML platforms such as Azure ML, AWS Sage Maker, or Google Vertex AI.
• Knowledge of MLOps tools and practices including MLflow, Kubeflow, or CI/CD pipelines.
• Experience working with large datasets and distributed computing frameworks such as Spark or Databricks.
• Strong data visualization and communication skills using Power BI, Tableau, or Python visualization libraries.
• Understanding of NLP, deep learning architectures and generative AI concepts.
• Ability to translate business problems into analytical solutions and measurable KPIs.
• Certification:
  Microsoft PL‑300:
  Power BI Data Analyst Certification. Any Cloud Certificate.