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.