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

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)

Data Scientist

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