Data Scientist

We are seeking a highly analytical Data Scientist to leverage data, statistical methods, and machine learning techniques to solve complex business problems. The ideal candidate will collect, analyze, and interpret large datasets, build predictive models, and communicate actionable insights to stakeholders. You will collaborate with engineering, product, business, and leadership teams to drive data-informed decision-making. Key Responsibilities

• Collect, clean, and analyze structured and unstructured datasets.
• Develop predictive models and machine learning algorithms to solve business challenges.
• Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies.
• Design, implement, and evaluate statistical models and experiments.
• Build data pipelines and automate data processing workflows.
• Develop dashboards, reports, and visualizations to communicate insights.
• Collaborate with data engineers, software developers, product managers, and business stakeholders.
• Validate model performance and continuously improve model accuracy.
• Deploy machine learning models into production environments.
• Document methodologies, findings, and technical solutions.
• Stay current with emerging trends in AI, machine learning, and data science.
• Participate in Agile ceremonies and contribute to cross-functional initiatives.

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
• 8+ years of experience in data science, machine learning, or advanced analytics.
• Strong proficiency in Python or R.
• Experience with SQL and database querying.
• Solid understanding of statistics, probability, and machine learning algorithms.
• Experience with data visualization tools such as Tableau, Power BI, or Matplotlib.
• Knowledge of data preprocessing, feature engineering, and model evaluation.
• Familiarity with cloud platforms (AWS, Azure, or Google Cloud Platform).
• Strong analytical, problem-solving, and communication skills., * Master's or Ph.D. in a quantitative discipline.
• Experience with deep learning frameworks such as TensorFlow or PyTorch.
• Knowledge of NLP (Natural Language Processing), Computer Vision, or Generative AI.
• Experience deploying ML models using Docker and Kubernetes.
• Familiarity with MLOps tools such as MLflow, Kubeflow, or SageMaker.
• Experience working with big data technologies such as Spark or Hadoop.
• Knowledge of A/B testing and experimental design.
• Experience with version control using Git.