Data Scientist

Job SummaryWe 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 ResponsibilitiesData Collection & PreparationIdentify, 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 EngineeringAnalyse data to understand distributions, relationships, correlations, and anomalies.Engineer features that improve model performance.Model Development & EvaluationBuild, test, and validate statistical and machine learning models (supervised, unsupervised, etc.).Use cross-validation, hyperparameter tuning; ensure models are robust.Deployment & MonitoringCollaborate with engineering / DevOps to deploy models in production or integrate into workflows / products.Monitor model performance, retrain, or update as needed; track drift.Visualization & ReportingCreate dashboards, reports, and visualizations to share findings with stakeholders.Translate complex results into clear, actionable insights for non-technical audiences.Business CollaborationWork 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 & EthicsEnsure data quality, security, privacy, and compliance with regulations.Maintain documentation, reproducibility, and versioning of models and analyses.Continuous Learning & InnovationStay updated with latest tools, algorithms, industry trends.Experiment with new methods / technologies to improve processes.Required Skills & QualificationsEducational BackgroundBachelor’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 SkillsProficiency 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 SkillsStrong 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 QualificationsExperience 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.