Data Scientist

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.

Required Skills & Qualifications

Education:

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or related fields. Ph.D. preferred but not mandatory.

Experience:

• 7–10 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.