Senior Data Scientist

About Us:

WorkGarden (formerly Astro Sirens) is an innovative, forward-thinking company committed to harnessing the power of data to drive strategic decisions and business transformation. We are looking for an experienced Data Scientist to join our team and help us deliver actionable insights that empower business growth. In this role, you'll leverage advanced machine learning, statistical techniques, and cutting-edge tools to unlock the value hidden in our data.

Requirements:
Responsibilities:

• Data Analysis & Modeling: Utilize advanced statistical methods and machine learning techniques to analyze large, complex datasets, uncovering insights and trends that inform business strategies.

• Predictive Analytics: Build predictive models to forecast business outcomes, optimize processes, and drive decision-making across various departments.

• Feature Engineering: Design and implement feature engineering techniques to improve the accuracy and performance of machine learning models.

• Collaboration: Work closely with cross-functional teams (business, product, engineering, etc.) to identify key business problems and translate them into data science solutions.

• Data Preparation: Clean, preprocess, and organize raw data from different sources to ensure it is suitable for analysis and modeling.

• Machine Learning Model Development: Develop and deploy machine learning models for classification, regression, clustering, and recommendation systems.

• Evaluation & Optimization: Evaluate the performance of models using various metrics (e.g., accuracy, precision, recall, F1 score) and optimize them for real-world performance.

• Data Visualization & Reporting: Create clear, insightful visualizations and reports to communicate findings to non-technical stakeholders.

• Automation: Automate repetitive data processing and reporting tasks to increase efficiency and reduce manual effort.

• Research & Innovation: Stay up-to-date with the latest developments in data science, machine learning, and AI, and bring new ideas and techniques to the team.

• Deployment & Monitoring: Implement models into production environments and monitor their performance over time to ensure they meet business requirements.

Requirements:

• Education: Bachelor's or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.

• Experience: Proven experience as a Data Scientist, with a strong background in machine learning, data analysis, and statistical modeling.

• Programming: Strong programming skills in Python (preferred), R, or similar languages. Experience with libraries like Pandas, NumPy, scikit-learn, TensorFlow, or PyTorch.

• Machine Learning: Expertise in building and deploying machine learning models for classification, regression, time-series forecasting, and NLP tasks.

• Data Processing: Experience with data wrangling, feature engineering, and working with large, unstructured datasets.

• SQL: Strong proficiency in SQL for querying databases and working with structured data.

• Cloud Platforms: Experience with cloud platforms such as AWS, Azure, or Google Cloud for deploying models and managing data pipelines.

• Data Visualization: Proficiency in data visualization tools such as Power BI, Tableau, or programming libraries like Matplotlib, Seaborn, or Plotly.

• Problem Solving: Strong analytical and problem-solving skills, with a passion for applying data science to real-world business challenges.

• Communication: Excellent communication skills to explain complex technical concepts to non-technical stakeholders and collaborate across teams.

Preferred Qualifications:

• Big Data Tools: Familiarity with big data tools such as Apache Spark, Hadoop, or Databricks.

• Deep Learning: Experience with deep learning techniques and frameworks (e.g., TensorFlow, Keras, PyTorch).

• Natural Language Processing (NLP): Experience with NLP techniques such as sentiment analysis, text classification, or language models.

• Statistical Analysis: Strong foundation in statistical analysis and hypothesis testing.

• Data Engineering: Experience with building and optimizing data pipelines for data collection, processing, and storage.

• Version Control: Familiarity with version control systems like Git.

• Business Acumen: Ability to understand business goals and align data science solutions to meet those objectives.

Benefits:
• Competitive salary and flexible payment methods.

• Opportunities for growth and professional development.

• Flexible working hours and remote work options.

• A collaborative, innovative, and inclusive work environment.

• Be a part of a data-driven culture that values impactful insights and decision-making.