Data Scientist

About the Role

We are seeking a highly motivated Data Scientist to join our team and drive data-driven decision-making through advanced analytics and machine learning models. The ideal candidate will have a strong technical background, a passion for uncovering insights from complex datasets, and the ability to collaborate with cross-functional teams to solve business challenges.

Key Responsibilities

• Design and implement machine learning models to predict trends, detect anomalies, and uncover actionable insights.
• Analyze structured and unstructured datasets to identify patterns and opportunities for optimization.
• Develop predictive models and algorithms for key business use cases, such as risk management, forecasting, and segmentation.
• Collaborate with data engineers and analysts to ensure data readiness for analysis and model deployment.
• Use statistical techniques to validate hypotheses, assess model performance, and improve decision-making processes.
• Create and present data-driven insights and recommendations to stakeholders in a clear and compelling manner.
• Deploy, monitor, and refine models in production environments to ensure scalability and reliability.
• Stay updated on the latest advancements in data science and machine learning, incorporating best practices into projects.

Qualifications

• Bachelors or Masters degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.
• Proven experience as a Data Scientist, with expertise in building and deploying machine learning models.
• Proficiency in Python or R, with experience in data manipulation libraries (e.g., Pandas, NumPy) and machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch).
• Strong knowledge of statistical methods, data modeling, and hypothesis testing.
• Experience working with large-scale data in environments such as Elasticsearch or SQL databases.
• Familiarity with data visualization tools (e.g., Kibana, Tableau, or Matplotlib) for presenting insights.
• Excellent problem-solving and analytical skills with attention to detail.
• Strong communication skills, with the ability to translate complex data findings into actionable insights for non-technical stakeholders.

Preferred Skills

• Hands-on experience with anomaly detection and forecasting models.
• Knowledge of big data tools and frameworks (e.g., Hadoop, Spark).
• Familiarity with MLOps practices and tools for deploying and managing machine learning models in production.
• Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) for data science workflows.
• Understanding of Elasticsearchs machine learning capabilities is a plus.