Data Scientist

Job Summary

We are seeking a Data Scientist who will turn raw data into actionable insights that drive business decisions. This includes collecting, cleaning, exploring, modeling, and interpreting large datasets; deploying predictive models; and communicating results to stakeholders. The ideal candidate will have strong statistical, programming, and analytical skills, along with an ability to translate business problems into data science solutions.

Key Responsibilities

• Data Collection & Preparation

• Identify, gather, and integrate data from multiple internal and external sources.
• Clean, preprocess, and validate data (structured and unstructured), handling missing values, outliers, etc.

• Exploratory Data Analysis (EDA) & Feature Engineering

• Analyse data to understand distributions, relationships, correlations, and anomalies.
• Engineer features that improve model performance.

• Model Development & Evaluation

• Build, test, and validate statistical and machine learning models (supervised, unsupervised, etc.).
• Use cross-validation, hyperparameter tuning; ensure models are robust.

• Deployment & Monitoring

• Collaborate with engineering / DevOps to deploy models in production or integrate into workflows / products.
• Monitor model performance, retrain, or update as needed; track drift.

• Visualization & Reporting

• Create dashboards, reports, and visualizations to share findings with stakeholders.
• Translate complex results into clear, actionable insights for non-technical audiences.

• Business Collaboration

• Work with business units (product, marketing, operations, etc.) to understand their needs, define metrics, recommend strategies.
• Suggest data-driven improvements, conduct experiments / A/B tests.

• Data Governance & Ethics

• Ensure data quality, security, privacy, and compliance with regulations.
• Maintain documentation, reproducibility, and versioning of models and analyses.

• Continuous Learning & Innovation

• Stay updated with latest tools, algorithms, industry trends.
• Experiment with new methods / technologies to improve processes.

Required Skills & Qualifications

• Educational Background

• Bachelor’s degree (or higher) in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative discipline.
• (Preferred) Master’s or PhD for more senior roles.

• Technical Skills

• Proficiency in languages such as

  Python

  or

  R

  for data analysis and modeling.
• Solid knowledge of

  SQL

  for data querying, extraction, manipulation.
• Experience with machine learning frameworks / libraries (e.g. scikit‑learn, TensorFlow, PyTorch, etc.).
• Data visualization skills (e.g.—Tableau, Power BI, Matplotlib, Seaborn, etc.).
• Experience working with large datasets; familiarity with big data tools/frameworks is a plus (Spark, Hadoop, etc.).
• Understanding of statistical methods (regression, classification, clustering, hypothesis testing, etc.).

• Soft Skills

• Strong analytical thinking, problem‑solving, and attention to detail.
• Good communication skills—able to explain technical subjects to non-technical stakeholders.
• Ability to work independently and collaboratively within cross-functional teams.
• Time management and ability to prioritize multiple tasks/projects.

Preferred / Nice-to-Have Qualifications

• Experience deploying models in production, monitoring, maintaining model performance over time.
• Familiarity with cloud platforms (AWS / Azure / GCP) for data storage / computation.
• Experience with data engineering / ETL pipelines.
• Knowledge of domain (your industry) is a plus (e.g. finance, healthcare, e‑commerce, etc.).
• Experience in experiment design (A/B testing), causal inference.
• Prior experience or interest in advanced methods (deep learning, NLP, computer vision, etc.), depending on role.