Data Science Developer Intermediate

Experience and Skill Set Requirements

Experience - 40 %

2 5 years of professional experience in data science data analytics or a related quantitative field (e.g. data engineering machine learning or business intelligence) or equivalent.

Proven experience in data analysis visualization and statistical modeling for real-world business or research problems.

Demonstrated ability to clean transform and manage large datasets using Python R or SQL.

Hands-on experience building and deploying predictive models or machine learning solutions in production or business environments.

Experience with data storytelling and communicating analytical insights to non-technical stakeholders.

Exposure to cloud environments (AWS Azure or GCP) and version control tools (e.g. Git).

Experience working in collaborative cross-functional teams ideally within Agile or iterative project structures.

Knowledge of ETL pipelines APIs or automated data workflows is an asset.

Previous work with dashboarding tools (Power BI Tableau or Looker) is preferred.

Technical Skills - 35%

Programming & Data Handling

Python (pandas NumPy scikit-learn statsmodels matplotlib seaborn)

SQL (complex queries joins aggregations optimization)

Data preprocessing (feature engineering missing data handling outlier detection)

Machine Learning & Statistical Modeling

Proficiency in supervised and unsupervised learning techniques (regression classification clustering dimensionality reduction)

Understanding of model evaluation metrics and validation techniques (cross-validation A/B testing ROC-AUC confusion matrix)

Basic understanding of deep learning frameworks (TensorFlow PyTorch or Keras) is a plus

Data Visualization & Reporting

Expertise with visualization libraries (matplotlib seaborn plotly or equivalent)

Experience building interactive dashboards (Tableau Power BI Dash or Streamlit)

Ability to design clear impactful data narratives and reports

Data Infrastructure & Tools

Experience with cloud-based data services (e.g. AWS S3 Redshift Azure Data Lake GCP BigQuery)

Experience working with big data frameworks such as Apache Spark and Hadoop for large-scale data processing.

Familiarity with data pipeline and workflow tools

Experience with API integration and data automation scripts (Selenium Python etc)

Solid grounding in probability statistics and linear algebra

Understanding of hypothesis testing confidence intervals and sampling methods

Soft Skills- 20%

Strong communication skills; both written and verbal

Ability to develop and present new ideas and conceptualize new approaches and solutions

Excellent interpersonal relations and demonstrated ability to work with others effectively in teams

Demonstrated ability to work with functional and technical teams Demonstrated ability to participate in a large team and work closely with other individual team members

Proven analytical skills and systematic problem solving

Strong ability to work under pressure work with aggressive timelines and be adaptive to change

Displays problem-solving and analytical skills using them to resolve technical problems

Public sector Experience- 5%

OPS(or other government) standards and processes

Must Have:

2 5 years of professional experience in data science data analytics or a related quantitative field (e.g. data engineering machine learning or business intelligence) or equivalent.

Proven experience in data analysis visualization and statistical modeling for real-world business or research problems.

Demonstrated ability to clean transform and manage large datasets using Python R or SQL.

Programming & Data Handling

Python (pandas NumPy scikit-learn statsmodels matplotlib seaborn)

SQL (complex queries joins aggregations optimization)

Data preprocessing (feature engineering missing data handling outlier detection)

Experience working with big data frameworks such as Apache Spark and Hadoop for large-scale data processing.