Applied Data Scientist

We are seeking an Applied Data Scientist to develop, deploy, and optimise machine learning models that solve real-world business problems. You will work closely with product managers, software engineers, and business stakeholders to translate data into actionable insights and production-ready AI solutions. Responsibilities: Analyse structured and unstructured datasets to identify trends and opportunities.Build, train, evaluate, and deploy machine learning and deep learning models.Develop predictive models for classification, regression, recommendation, and forecasting.Perform feature engineering, data preprocessing, and model validation.Design and implement data pipelines for scalable ML workflows.Collaborate with engineering teams to deploy models into production using MLOps practices.Monitor model performance, detect drift, and retrain models as needed.Communicate findings through dashboards, reports, and presentations.Stay current with advancements in AI, machine learning, and generative AI. Requirements: Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.2-5+ years of experience in data science or machine learning.Strong programming skills in Python.Experience with SQL and relational databases.Solid understanding of statistics, probability, and machine learning algorithms.Experience with data visualisation tools such as Tableau, Power BI, or Matplotlib.Familiarity with cloud platforms (AWS, Azure, or GCP). Technical Skills: Languages: Python, SQLLibraries: Pandas, NumPy, Scikit-learn, XGBoostDeep Learning: TensorFlow or PyTorchVisualisation: Matplotlib, Seaborn, PlotlyBig Data: Spark (preferred)MLOps: MLflow, Docker, Kubernetes, AirflowVersion Control: GitCloud: AWS SageMaker, Azure ML, or Vertex AI Preferred Skills: Experience with NLP, computer vision, or recommendation systems.Knowledge of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and prompt engineering.Experience building AI applications using LangChain, LlamaIndex, or similar frameworks.Familiarity with vector databases such as Pinecone, Weaviate, Milvus, or FAISS.Understanding of CI/CD for machine learning pipelines. Soft Skills: Strong analytical and problem-solving abilities.Excellent communication and presentation skills.Ability to collaborate across cross-functional teams.Business acumen and stakeholder management.Self-driven with a continuous learning mindset. Preferred Experience: Deploying machine learning models to production.Working with end-to-end ML pipelines.A/B testing and experimentation.Experience with generative AI applications is a plus. Success Metrics: Improved model accuracy and business KPIs.Reliable, scalable production ML systems.Reduced model deployment time.Actionable insights that drive business decisions. .