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.