Senior Data Scientist

1. Data Strategy & Leadership

1. Project Leadership: Leading data science projects from ideation to deployment, managing project timelines, resources, and ensuring alignment with business goals.
2. Mentoring & Coaching: Guiding and mentoring junior data scientists and data analysts, providing technical guidance, reviewing work, and offering professional development advice.
3. Stakeholder Collaboration: Working closely with business stakeholders (e.g., marketing, finance, product teams) to understand their objectives and translate them into data-driven solutions.
4. Strategic Input: Providing strategic recommendations based on data analysis and insights to help drive business decisions and long-term goals.

2. Advanced Data Analysis & Modeling

1. Data Preprocessing: Overseeing data collection, cleaning, transformation, and feature engineering to ensure data is prepared for analysis and modeling.
2. Machine Learning: Building and deploying machine learning models for tasks such as classification, regression, clustering, or recommendation systems.
3. Statistical Analysis: Applying statistical methods to identify patterns, test hypotheses, and validate models.
4. Big Data Technologies: Leveraging big data tools and frameworks like Hadoop, Spark, or Google BigQuery to analyze large datasets.
5. Model Optimization: Tuning models for better performance by adjusting hyperparameters and applying cross-validation techniques.

3. Data Visualization & Reporting

1. Data Visualization: Creating clear, intuitive, and interactive visualizations to communicate complex data findings.
2. Dashboard Development: Designing and maintaining dashboards that track key performance indicators (KPIs).
3. Report Generation: Preparing detailed reports and presentations to communicate insights, model results, and business recommendations.

4. Advanced Statistical & Mathematical Techniques

1. Statistical Modeling: Applying advanced statistical techniques to interpret data and derive business insights.
2. Optimization & Simulation: Using optimization techniques for decision-making and resource allocation.
3. Deep Learning: Designing and implementing deep learning models for complex tasks.

5. Product Development & Deployment

1. Model Deployment: Overseeing the deployment of machine learning models into production environments.
2. Model Monitoring & Maintenance: Continuously monitoring the performance of deployed models and improving them based on new data.
3. Collaboration with Engineering Teams: Working closely with data engineers and software developers to implement and scale models.

6. Research & Innovation

1. Staying Current: Keeping up-to-date with the latest developments in data science, machine learning, and AI.

Technical skills required

1. Proficient throughout the Machine Learning life cycle
2. End to end development; creation to deployment
3. Cloud experience; AWS, Azure, GCP
4. MLOPs
5. Data Engineering pipelines
6. Software Engineering experience is a bonus; Python or Java

Experience required

1. Worked on products that have gone into real settings
2. Take end to end ownership of all ML features
3. Implement key machine learning strategies across the business
4. Collaborate and communicate effectively

Education experience

1. PhD or MSc is highly desirable (STEM; Com Sci, Maths, Statistics, Fin Maths, Physics etc.)

What you'll get in return

1. Visa
2. Medical benefits + family
3. Loans and credit facilities available
4. Yearly Bonus
5. Relocation allowance (cash)
6. Return flight tickets yearly
7. Hotel stay paid for when first arrive in the country