Job Description
1. Data Analysis:
- Analyze large, complex datasets to identify trends, patterns, and correlations.
- Utilize statistical methods and machine learning techniques to extract actionable insights.
2. Model Development:
- Develop predictive models to forecast future trends and outcomes.
- Implement machine learning algorithms for classification, regression, and clustering.
3. Feature Engineering:
- Engineer features and variables to enhance model performance.
- Collaborate with domain experts to extract relevant features from data.
4. Data Visualization:
- Create visually appealing and informative data visualizations to communicate findings.
- Present insights and recommendations to non-technical stakeholders.
5. Data Cleaning and Preprocessing:
- Clean and preprocess raw data to ensure accuracy and consistency.
- Handle missing data and outliers effectively.
6. Collaboration:
- Work closely with cross-functional teams, including engineers, business analysts, and domain experts.
- Collaborate with stakeholders to understand business objectives and requirements.
- Continuous Learning:
- Stay abreast of the latest advancements in data science, machine learning, and related technologies.
- Apply new methodologies and techniques to improve model accuracy and efficiency.
QUALITY AND REQUIREMENTS
- Bachelor’s or Master’s in Computer Science, Statistics, Mathematics, or a related field.
- 3+ years of proven experience as a Data Scientist.
- Strong proficiency in programming languages such as Python or R.
- Experience with data manipulation and analysis libraries (e.g., Pandas, NumPy, SciPy).
- Solid understanding of machine learning algorithms and statistical modeling techniques.
- Proficiency in data visualization tools (e.g., Matplotlib, Seaborn, Tableau).
- Strong problem-solving and critical-thinking skills.
- Excellent communication and presentation skills.
- Ability to work independently and collaboratively in a fast-paced environment.
Bonus Skills:
- Experience with deep learning frameworks (e.g., TensorFlow, PyTorch, NumPy).
- Knowledge of big data technologies (e.g., Hadoop, Spark).
- Familiarity with cloud platforms (e.g., AWS, Azure).
- Industry-specific domain knowledge (e.g., finance, healthcare).
- Certification in data science or related field.