Tweedle.ai is on a mission to revolutionize the use of data in everyday healthcare tasks. Our goal is to enhance the quality of patient engagement and transform every interaction, the healthcare team has with the patient.
Reports and dashboards alone often fall short, especially for dynamic, repetitive tasks that follow the law of diminishing marginal utility. When multiple individuals perform these tasks without grasping their underlying value, they can easily be overlooked. In a work setting, this can lead to reduced efficiency, lower engagement, and ultimately, patient attrition, diminished lifetime value, inefficiencies in business processes, and a decline in the quality of patient experience or treatment outcomes.
We are looking for a Sr.Data Scientist with past experience in healthcare. Since these are fairly early days in Tweedle's journey, this can lead to an opportunity to become a co-founder. You can consider working on this project on a part-time basis until the product goes into beta.
As a Data Scientist, your role in the project may involve understanding the business and uncovering the intrinsic value within the data that can be leveraged for patient engagement and decision-making. Some examples of insights might include the following:
Causation: By carefully analyzing data, particularly through experiments or advanced statistical techniques, we can sometimes determine whether one factor is causing another, not just correlated with it.
Trends: Data can reveal long-term movements or directions in variables over time, helping to predict future outcomes or behaviors.
Anomalies and Outliers: Data can identify unusual or unexpected observations that deviate from the norm, which might signal errors, unique cases, or areas that need special attention.
Forecasts and Predictions: With the right models, data can be used to predict future events or behaviors, aiding in decision-making and strategic planning.
Behavioral Insights: Data can help understand human behavior, preferences, and decision-making processes, especially when analyzing data from social media, consumer purchases, or user interactions.
Segmentation: Data can be used to categorize or segment populations, customers, or other entities into distinct groups based on shared characteristics, allowing for more targeted approaches.
Efficiency and Optimization: By analyzing data, businesses can identify inefficiencies and areas where resources could be better utilized, leading to optimization of processes and cost savings.
Risk Assessment: Data can be used to evaluate potential risks, helping organizations anticipate and mitigate possible negative outcomes.
Feedback and Improvement: Continuous data collection and analysis can provide feedback on performance, helping to identify areas for improvement and monitor the effectiveness of changes.
Sentiment Analysis: Especially with textual data, sentiment analysis can reveal public or customer sentiment, opinions, and emotional responses to products, services, or events.
We are currently executing the steps that are crucial for building a robust foundation on which accurate, reliable, and scalable models can be created. You may be required to assist the technology team in some of the steps mentioned below.
Data Collection and Ingestion
Identifying Data Sources: Determine where the data is coming from, such as databases, APIs, sensors, web scraping, or manual entry.
- Data Acquisition: Extract or collect the data from the identified sources. This could involve setting up connections to databases, API calls, or automated scraping.
- Data Ingestion: Load the data into a system for processing. This might involve batch loading, real-time streaming, or a combination of both.
Data Cleaning and Preprocessing
- Data Quality Assessment: Evaluate the completeness, accuracy, and consistency of the data. Identify missing values, duplicates, and inconsistencies.
- Data Cleaning: Handle missing data (e.g., imputation, removal), correct inaccuracies, and remove duplicates. This may also involve normalizing or standardizing data values.
- Outlier Detection and Removal: Identify and address any outliers or anomalies that could skew the model.
- Data Transformation: Convert data into a suitable format or structure, which may involve:
- Normalization/Standardization: Rescale numerical data to a common scale.
- Encoding: Convert categorical data into numerical formats (e.g., one-hot encoding).
- Aggregation: Summarize or aggregate data points to a higher level (e.g., daily totals).
- Feature Engineering: Create new features or variables that might enhance the predictive power of the model.
Data Integration and Merging
- Data Joining: Combine data from multiple sources or tables into a single, unified dataset.
- Data Matching: Ensure that data from different sources can be accurately linked, often by matching on keys like IDs or timestamps.
- Data Alignment: Synchronize datasets, especially time-series data, to ensure they are aligned properly for analysis.
Data Pipeline Creation
- Pipeline Design: Plan and design a data pipeline that automates the flow of data from raw sources to the final model input.
- Pipeline Implementation: Build and deploy the pipeline to handle data extraction and transformation.
Data Storage and Management
- Data Storage Selection: Choose appropriate storage solutions (e.g., relational databases, NoSQL databases, data lakes, cloud storage) depending on the data type, volume, and access requirements.
- Data Organization: Organize the data in a way that is efficient for retrieval and analysis (e.g., partitioning, indexing).
- Data Format: Store data in a format that is readable and efficient for modeling (e.g., CSV, Parquet, Avro).
If you're interested in this role, we’re excited to meet you. Please note that this is not a full-time position at the moment, but it has the potential to become one once our beta release moves into testing. We’ve secured seed capital to support our current efforts and already have customers and additional funding lined up, contingent on proving the product's value and effectiveness.
