- Data Collection and Preparation: Gather and preprocess large volumes of structured and unstructured data from various sources, including databases, APIs, web scraping, and sensor data.
- Exploratory Data Analysis (EDA): Conduct exploratory data analysis to understand the characteristics, patterns, and relationships within the data. Visualize data using statistical techniques and data visualization tools to identify trends and anomalies.
- Statistical Analysis: Apply statistical methods and hypothesis testing to analyze data, validate assumptions, and draw meaningful insights. Perform descriptive and inferential statistics to quantify relationships and make predictions.
- Machine Learning Modeling: Develop and implement machine learning models, algorithms, and predictive analytics solutions to solve business problems and optimize processes. Choose appropriate modeling techniques based on data characteristics and problem requirements.
- Feature Engineering: Engineer and select relevant features from raw data to improve model performance and generalization. Perform feature selection, transformation, and dimensionality reduction techniques to enhance model interpretability and efficiency.
- Model Evaluation and Validation: Evaluate model performance using appropriate metrics and validation techniques, such as cross-validation, holdout validation, and confusion matrix analysis. Fine-tune model hyperparameters to optimize performance and prevent overfitting.
- Model Deployment: Deploy machine learning models into production environments, integrate models with existing systems and workflows, and monitor model performance over time. Ensure scalability, reliability, and security of deployed models.
- Collaboration and Communication: Collaborate with cross-functional teams, including data engineers, software developers, and business stakeholders, to understand requirements, prioritize projects, and deliver solutions. Communicate technical concepts and findings to non-technical audiences effectively.
- Continuous Learning: Stay abreast of the latest developments in data science, machine learning, and related fields. Participate in training, conferences, and online courses to enhance skills and knowledge.
- Education: Master's or Ph.D. degree in Computer Science, Statistics, Mathematics, Engineering, or a related field. Specialization in data science, machine learning, or artificial intelligence is preferred.
- Experience: Proven experience in data science roles, with a track record of applying statistical analysis and machine learning techniques to real-world problems. Experience with data visualization tools (e.g., Matplotlib, Seaborn) and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) is required.
- Programming Skills: Proficiency in programming languages commonly used in data science, such as Python or R. Experience with SQL for data manipulation and querying is beneficial.
- Statistical Knowledge: Strong understanding of statistical concepts and methods, including probability theory, hypothesis testing, regression analysis, and time series analysis.
- Machine Learning Expertise: In-depth knowledge of machine learning algorithms, including supervised learning (e.g., linear regression, decision trees, support vector machines) and unsupervised learning (e.g., clustering, dimensionality reduction).
- Data Engineering Skills: Familiarity with data preprocessing techniques, data wrangling, and feature engineering. Experience with big data technologies (e.g., Hadoop, Spark) and distributed computing frameworks is a plus.
- Problem-Solving Abilities: Strong analytical and problem-solving skills, with the ability to frame business problems, formulate hypotheses, and design experiments to test hypotheses empirically.
- Communication Skills: Excellent communication and interpersonal skills, with the ability to work collaboratively in multidisciplinary teams and present findings and recommendations to stakeholders effectively.
- Ethical Standards: High level of integrity and ethical conduct in handling sensitive data and ensuring privacy and security compliance.
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Urgently Hire Need-Data Science Specialist II - Atlanta, United States - 978 Investment
Description
As a Data Science Specialist, you will be responsible for leveraging data analysis and machine learning techniques to extract insights, solve complex problems, and drive decision-making processes within the organization. Here's a detailed job description for this role:
Responsibilities:
Requirements:
