Senior Data Scientist

Roles and responsibilities

1. Data Strategy & Leadership

• Project Leadership: Leading data science projects from ideation to deployment. This includes managing project timelines, resources, and ensuring alignment with business goals.
• Mentoring & Coaching: Guiding and mentoring junior data scientists and data analysts to help them grow in their roles. This involves providing technical guidance, reviewing work, and offering professional development advice.
• Stakeholder Collaboration: Working closely with business stakeholders (e.g., marketing, finance, product teams) to understand their objectives and translate them into data-driven solutions.
• 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

• Data Preprocessing: Overseeing data collection, cleaning, transformation, and feature engineering to ensure the data is prepared for analysis and modeling.
• Machine Learning: Building and deploying machine learning models for tasks such as classification, regression, clustering, or recommendation systems. This includes supervised and unsupervised learning, deep learning, and ensemble methods.
• Statistical Analysis: Applying statistical methods to identify patterns, test hypotheses, and validate models. This could include A/B testing, hypothesis testing, and time-series analysis.
• Big Data Technologies: Leveraging big data tools and frameworks like Hadoop, Spark, or Google BigQuery to analyze large datasets that cannot be handled by traditional systems.
• Model Optimization: Tuning models for better performance by adjusting hyperparameters, experimenting with different algorithms, and applying cross-validation techniques.

3. Data Visualization & Reporting

• Data Visualization: Creating clear, intuitive, and interactive visualizations to communicate complex data findings. This can include using tools like Tableau, Power BI, Matplotlib, Seaborn, or Plotly.
• Dashboard Development: Designing and maintaining dashboards that track key performance indicators (KPIs) and provide stakeholders with real-time data insights.
• Report Generation: Preparing detailed reports and presentations to communicate insights, model results, and business recommendations to both technical and non-technical stakeholders.

4. Advanced Statistical & Mathematical Techniques

• Statistical Modeling: Applying advanced statistical techniques (e.g., linear regression, logistic regression, Bayesian analysis, etc.) to interpret data and derive business insights.
• Optimization & Simulation: Using optimization techniques (e.g., linear programming, Monte Carlo simulations) for decision-making and resource allocation.
• Deep Learning: Designing and implementing deep learning models for more complex tasks like image recognition, natural language processing (NLP), or autonomous systems.

5. Product Development & Deployment

• Model Deployment: Overseeing the deployment of machine learning models into production environments and ensuring they are scalable, maintainable, and integrated with other systems (e.g., via AWS, Azure, or Google Cloud platforms).
• Model Monitoring & Maintenance: Continuously monitoring the performance of deployed models, troubleshooting issues, and improving models based on new data or feedback.
• Collaboration with Engineering Teams: Working closely with data engineers and software developers to implement and scale models into production environments efficiently.

6. Research & Innovation

• Staying Current: Keeping up-to-date with the latest developments in data science, machine learning, and AI, and exploring how these advancements can be applied to solve business problems.

Desired candidate profile

Technical skills required

• Proficient throughout the Machine Learning life cycle
• End to end development; creation to deployment
• Cloud experience; AWS, Azure, GCP
• MLOPs
• Data Engineering pipelines
• Software Engineering experience is a bonus; Python or Java

Experience required

• Worked on products that have gone into real settings
• Take end to end ownership of all ML features
• Be able to implement key machine learning strategies across the business and work with stake holders effectively
• Be able to collaborate, communicate effectively and coordinate end to end delivery

Education experience

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

What you'll get in return

• Visa
• Medical benefits + family
• Loans and credit facilities available
• Yearly Bonus
• Relocation allowance (cash)
• Return flight tickets yearly
• Hotel stay paid for when first arrive in the country