Data Scientist

About the job

Responsibilities:

Data Exploration and Preparation: Collecting, cleaning, and preprocessing large volumes of structured and unstructured data from various sources to ensure data quality and suitability for analysis.

Statistical Analysis and Modeling: Applying statistical techniques to analyze data, identify patterns, correlations, and trends, and develop predictive models that provide insights and support decision-making.

Machine Learning and Data Mining: Utilizing machine learning algorithms and data mining techniques to discover hidden patterns, build predictive models, and develop algorithms for automation and optimization.

Feature Engineering: Identifying and creating relevant features from raw data that enhance the performance and accuracy of predictive models.

Model Development and Evaluation: Designing, implementing, and fine-tuning machine learning models and algorithms, assessing their performance, and iterating on them to improve accuracy and efficiency.

Data Visualization: Creating visual representations of data analysis results to effectively communicate insights and findings to non-technical stakeholders.

Collaborating with cross-functional teams: Working closely with domain experts, business stakeholders, and other data professionals to understand business objectives, develop data-driven solutions, and drive implementation.

Experiment Design and A/B Testing: Designing and conducting experiments to validate hypotheses, measure the impact of changes, and optimize business processes.

Data Governance and Ethics: Ensuring compliance with data privacy regulations, maintaining data integrity and security, and promoting ethical use of data.

Qualifications:

Strong Analytical and Mathematical Skills: Proficiency in statistical analysis, mathematical modeling, and quantitative reasoning to formulate and solve complex problems.

Programming Skills: Proficiency in programming languages such as Python, R, or Java, and experience with data manipulation libraries and frameworks (e.g., NumPy, pandas, scikit-learn) for efficient data analysis and model development.

Machine Learning and Data Mining: Solid understanding of machine learning algorithms, data mining techniques, and their practical applications for predictive modeling, classification, clustering, and recommendation systems.

Data Visualization: Proficiency in data visualization tools and libraries (e.g., Tableau, Matplotlib, ggplot) to create meaningful visual representations of data analysis results.

Big Data Technologies: Familiarity with big data processing frameworks like Apache Hadoop, Spark, or distributed computing platforms, and experience working with large-scale datasets.

Domain Knowledge: Expertise in the specific industry or domain relevant to the organization, such as finance, healthcare, e-commerce, or marketing, to understand business problems and develop tailored solutions.

Problem-Solving Abilities: Strong critical thinking skills to identify data-related challenges, develop innovative approaches, and apply creative problem-solving techniques.

Communication and Presentation Skills: Excellent verbal and written communication skills to convey complex technical concepts to both technical and non-technical stakeholders, and to present data-driven insights effectively.