Hire -Data Science Specialist II (Full-Time)

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:

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.

Requirements:

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.