Senior Data Scientist

We are looking for a talented Data Scientist to join our team and help us unlock the power of data to drive key business decisions. The ideal candidate will have strong analytical skills, experience in statistical modeling, and expertise in machine learning techniques to extract insights, make predictions, and drive optimization across business functions.

Responsibilities:

• Data Analysis and Insights: Analyze large and complex datasets to extract actionable insights that help drive business strategies and decision-making.
• Model Development: Develop, validate, and implement predictive and prescriptive models using machine learning, statistical, and data mining techniques.
• Experimentation: Design and conduct experiments (e.g., A/B testing) to optimize processes, measure impacts, and test hypotheses.
• Data Wrangling: Clean, preprocess, and transform data from various sources, preparing it for analysis and modeling.
• Feature Engineering: Identify and create features that enhance the performance of machine learning models.
• Collaborate with Stakeholders: Work with product managers, engineers, and other business stakeholders to understand data needs, gather requirements, and deliver insights that drive strategic decision-making.
• Deploy Models: Collaborate with engineering teams to deploy and integrate models into production systems, ensuring scalability and efficiency.
• Data Visualization and Reporting: Create visualizations, dashboards, and reports that communicate data findings clearly and effectively to non-technical audiences.
• Continuous Learning: Stay up to date with the latest advancements in data science, machine learning, and AI technologies to ensure our solutions remain cutting-edge.

Required Skills and Qualifications:

• Experience with Statistical Analysis: Strong knowledge of statistical concepts, hypothesis testing, and data analysis techniques.
• Machine Learning: Proficiency in machine learning algorithms, including supervised and unsupervised learning, regression, classification, clustering, and recommendation systems.
• Programming Skills: Strong programming skills in Python or R, including libraries like scikit-learn, TensorFlow, PyTorch, pandas, and NumPy.
• Data Visualization Tools: Experience with data visualization tools such as Tableau, Power BI, or libraries like Matplotlib, Seaborn, and Plotly.
• Data Manipulation: Strong SQL skills for data extraction, transformation, and analysis.
• Big Data Technologies: Familiarity with big data frameworks like Hadoop, Spark, and cloud-based data storage solutions (e.g., AWS S3, Google BigQuery).
• Statistical Software: Experience with statistical software like SAS, R, or SPSS is a plus.
• Communication Skills: Ability to present complex findings in a clear and concise way to both technical and non-technical stakeholders.
• Deep Learning: Knowledge of deep learning techniques, including CNNs, RNNs, or transformers.
• Natural Language Processing (NLP): Experience with NLP methods and tools (e.g., NLTK, SpaCy, or Hugging Face transformers).
• Time Series Analysis: Experience in analyzing time-series data and building predictive models for forecasting.
• Experiment Design: Knowledge of experimental design and methodologies for A/B testing.
• Cloud Platforms: Experience working with cloud platforms like AWS, Azure, or Google Cloud, particularly their machine learning and data services.
• Bachelor's or Master's Degree: in Data Science, Statistics, Mathematics, Computer Science, or a related field.
• Analytical Skills: Strong analytical and problem-solving skills with a focus on actionable insights.
• Attention to Detail: High level of accuracy and attention to detail in data analysis and model building.
• Business Acumen: Ability to understand and prioritize business needs, aligning data science efforts with strategic objectives.
• Adaptability: Ability to work in a fast-paced environment and handle multiple projects simultaneously.