Job details
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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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 stakeholders effectively
- Be able to collaborate, communicate effectively and coordinate end to end delivery
- PhD or MSc is highly desirable (STEM; Com Sci, Maths, Statistics, Fin Maths, Physics etc.)
- 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
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Hiring company
Hays
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