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, SQL
• Libraries: Pandas, NumPy, Scikit-learn, XGBoost
• Deep Learning: TensorFlow or PyTorch
• Visualisation: Matplotlib, Seaborn, Plotly
• Big Data: Spark (preferred)
• MLOps: MLflow, Docker, Kubernetes, Airflow
• Version Control: Git
• Cloud: 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.