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. Education **Required Skills & Qualifications** • Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or related fields. Ph.D. preferred but not mandatory. Experience • 10-15 years of experience in data science, advanced analytics, or applied machine learning roles. 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). Preferred Skills • Experience with NLP, computer vision, or time-series forecasting. • Familiarity with data warehousing and ETL/ELT concepts (e.g., Snowflake, Redshift, BigQuery). • Exposure to deep learning frameworks such as TensorFlow, PyTorch, or Keras. • Knowledge of model governance, data ethics, and responsible AI principles. • Experience leading or mentoring junior data scientists or analysts. Key Attributes • Strong analytical thinking and problem-solving ability. • Excellent communication and storytelling skills. • Ability to translate complex data insights into actionable business recommendations. • Passion for experimentation, innovation, and continuous learning. • Collaborative mindset with cross-functional teams. **Key Performance Indicators (KPIs)** • Model performance metrics (accuracy, recall, precision, AUC, etc.). • Business impact of deployed models (ROI, cost savings, revenue growth). • Speed and quality of project delivery. • Adoption and scalability of data science solutions. • Contribution to innovation, automation, and process improvement.