RESPONSIBILITIES
1. Data Collection and Cleaning: Gather data from various internal and external sources and ensure that it is clean, consistent, and ready for analysis. This includes identifying and addressing missing values, outliers, and anomalies.
2. Exploratory Data Analysis: Conduct thorough exploratory data analysis (EDA) to understand the underlying patterns, trends, and relationships within the data. This step is crucial for formulating hypotheses and guiding the modelling process.
3. Model Development: Create predictive models using statistical and machine learning techniques to address business challenges. This involves selecting appropriate algorithms, training models, and validating results to ensure accuracy.
4. Data Visualization: Present complex data findings in an easily digestible format using data visualization tools. Effective visualizations help stakeholders grasp insights quickly and aid in strategic decision-making.
5. Collaboration and Communication: Work closely with cross-functional teams, including product managers, engineers, and executives, to understand their data needs and provide actionable insights. Clear communication of findings and recommendations is essential for driving business outcomes.
6. Continuous Learning and Innovation: Stay updated with the latest trends, tools, and techniques in data science to continuously enhance analytical capabilities and explore innovative solutions.
TECHNICAL SKILL SET
1. Proficiency in programming languages such as Python, R, or SQL.
2. Strong understanding of machine learning algorithms and frameworks (e.g., Scikit-Learn, TensorFlow, or PyTorch).
3. Familiarity with data visualization tools such as Qlik, Tableau, Power BI, or Matplotlib.
4. Experience with database management systems (e.g., SQL Server, MySQL, or MongoDB).
5. Knowledge of statistical analysis and research methodologies.
SOFT SKILL SET
1. Problem-Solving: Strong analytical skills to solve complex problems and drive data-driven decisions.
2. Communication: Excellent communication skills to articulate complex data concepts to a variety of stakeholders.
3. Collaboration: Proven ability to work well in a team-oriented environment with cross-functional teams.
4. Attention to Detail: Keen attention to detail in data analysis and troubleshooting.
5. Time Management: Ability to manage multiple priorities and projects in a fast-paced environment
WORK EXPERIENCE
1. Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.
2. Minimum of 3 years of experience in data science or a related field.
3. Proven experience in data analysis, statistical modelling, and machine learning.
4. Experience working with large datasets and big data technologies is preferred.
5. Experience in a specific industry (e.g., finance, healthcare, retail) is a plus.
6. Fresh graduates are encouraged to apply.
