Data Scientist
Our data scientists uncover critical patterns, trends, and correlations that provide deep insights into customer behavior, market dynamics, and operational performance. By mastering data analysis and predictive modeling, they deliver the strategic insights you need to optimise operations, drive innovation, enhance customer experiences, and mitigate risk securing a lasting competitive advantage.
About This Role
Sonaqode's Data Scientists transform complex data into a decisive competitive advantage. They build sophisticated predictive models using cutting-edge machine learning and statistical techniques to deliver high value business insights with exceptional accuracy. Our approach is rooted in meticulous data quality and continuous algorithm optimisation, ensuring every model delivers reliable, actionable intelligence tailored to your strategic objectives.
Our dedicated team brings deep expertise in machine learning, deep learning, and statistical modeling, enhanced by specialised knowledge in AI, NLP, and large scale data ecosystems. They provide more than just analysis; they deliver data driven strategies that help you navigate market complexity and seize new opportunities. With a proven track record of building scalable, robust analytical solutions for both niche markets and global enterprises, we equip your business with the intelligence to lead. Partner with us to make data your most powerful asset.
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Skill Set
Technical Skills
- Programming: Proficient in Python and R, with knowledge of libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch.
- Statistics and Probability: Strong foundation in statistical methods, hypothesis testing, and probability theory.
- Machine Learning: Expertise in various algorithms, including supervised, unsupervised, and reinforcement learning.
- Data Munging and Cleaning: Proficient in handling messy data, including data cleaning, transformation, and exploration.
- Data Visualisation: Ability to create compelling visualisations using tools like Python libraries Matplotlib, Seaborn or specialised software Tableau, Power BI.
- Big Data Technologies: Knowledge of tools like Hadoop, Spark, and cloud based data platforms.
- Database Management: Proficient in SQL.
- Cloud Computing: Familiar with cloud platforms AWS, Azure, GCP for data storage and processing.
Experience
- Data modeling and analysis projects: Experience in building predictive models and deriving actionable insights.
- Machine learning: Ability to build and deploy predictive models.
- Industry knowledge: Good understanding of data within various domains e.g. Finance, healthcare, marketing.
- Data engineering experience: Experience with working with large datasets and building data pipelines.
- Cloud platform usage: Leveraging cloud based data services for efficient data processing.
- Data visualisation: Creating effective visualisations to communicate findings efficiently.
Key Deliverables
Data Exploration and Preparation
- Data Cleaning and Preprocessing: Handling missing values, outliers, and inconsistencies in data.
- Data Exploration: Discovering patterns, trends, and relationships within data.
Model Building and Evaluation
- Model Selection: Choosing appropriate algorithms and techniques for the problem.
- Model Training: Developing and training machine learning models.
- Model Evaluation: Assessing model performance using relevant metrics.
Data Product Development
- Data Pipelines: Building automated data pipelines for model deployment and retraining.
- Data Products: Creating data driven applications or tools.
Insights and Communication
- Data Storytelling: Effectively communicating insights to stakeholders through visualisations and reports.
- Predictive Modeling: Developing models to forecast future trends or outcomes.
- Business Impact Analysis: Quantifying the impact of data driven insights on business outcomes.