Upwork is hiring a Data Prediction Analyst (Python)

We are seeking a skilled and analytical Data Prediction Analyst to join our team. As a Data Prediction Analyst, you will be responsible for leveraging advanced statistical modeling and machine learning techniques to analyse large datasets, develop predictive models, and generate valuable insights to drive informed business decisions. Proficiency in Python and a deep understanding of statistical analysis and predictive modeling are essential for success in this role.

Responsibilities:

1.Data Analysis: Perform exploratory data analysis on large datasets to identify patterns, trends, and relationships that can inform predictive models.

2. Predictive Modeling: Develop and implement predictive models using advanced statistical techniques and machine learning algorithms in Python.

3. Feature Engineering: Identify relevant features and engineer new features from existing data to improve model accuracy and performance.

4. Data Visualization: Present data-driven insights and predictions through clear and compelling visualizations, such as charts, graphs, and dashboards.

5. Documentation: Document all analyses, methodologies, and results, ensuring the reproducibility and transparency of the work.

6. Stay Updated: Keep abreast of the latest advancements in data analysis, machine learning, and predictive modeling techniques, and apply them to improve existing methodologies.

Qualifications:

1. Education: Bachelor's or Master's degree in a relevant field such as Data Science, Statistics, Computer Science, or Mathematics.

2. Experience: Previous experience as a Data Analyst or Data Scientist, with a focus on predictive modeling and analysis.

3. Programming Skills: Proficiency in Python is a must, along with experience using libraries such as NumPy, Pandas, Scikit-learn, and TensorFlow/PyTorch.

4. Statistical Analysis and Modeling: Strong understanding of statistical analysis, regression, classification, clustering, time series analysis, and other relevant predictive modeling techniques.

5. Machine Learning: Experience with machine learning algorithms, including decision trees, random forests, support vector machines, neural networks, and ensemble methods.

6. Data Visualization: Proficient in data visualization tools and libraries such as Matplotlib, Seaborn, or Plotly to effectively communicate insights.