5 Data Harmonization Jobs

You will be responsible for applying Natural Language Processing (NLP) AI techniques, conducting machine learning, and developing high-quality prediction systems for data classification. Your main tasks will include presenting information through data visualization techniques, as well as collecting, preprocessing, and harmonizing data. As a part of your role, you will develop applications in machine learning and artificial intelligence, while selecting features, building, and optimizing classifiers using machine learning techniques. Understanding business objectives and creating models to support them, along with relevant metrics to monitor progress, will be crucial. You will need to manage available resources efficiently, such as hardware, data, and personnel, to ensure project deadlines are met. Your responsibilities will also involve analyzing various ML algorithms to identify the most suitable ones for solving a specific problem and ranking them based on success probability. Exploring and visualizing data to comprehend it better, detecting variations in data distribution that could impact model performance in real-world deployment, and verifying data quality through cleaning processes will be essential. You will supervise the data acquisition process if additional data is required, search for relevant datasets online for training purposes, define validation strategies, and determine pre-processing or feature engineering on datasets. Furthermore, you will be involved in defining data augmentation pipelines, training models, tuning hyperparameters, analyzing model errors, and developing strategies to address them. Deployment of models to production environments will also be a key aspect of your role. As a desired candidate, you should possess a strong understanding of machine learning (ML) and deep learning (DL) algorithms, as well as an architectural comprehension of CNN and RNN algorithms. Experience with NLP data models and libraries, entity extraction using NLP, and proficiency in tools like TensorFlow, scikit-learn, and spaCy is required. Additionally, familiarity with transfer learning, scripting and programming skills in Python and Streamlit, common data science toolkits such as NumPy and Fast.AI, and various machine learning techniques and algorithms like k-NN, Naive Bayes, and SVM is essential. Proficiency in query languages, applied statistics skills, data wrangling, data exploration, as well as tools like Tableau and DataPrep will be advantageous. The ideal candidate will be an immediate joiner with no restrictions on salary for the right individual.,

The role requires you to be experienced in rolling out Sales Force Automation (SFA) & Distributor Management System (DMS). You should have a keen eye for identifying new developments to enhance the adoption of both DMS & SFA. Providing end to end support to distributors and field force for adoption, daily usage, and organizing periodic training sessions will be a crucial part of your responsibilities. Collaborating effectively with IT teams, leading with solutions to ensure data harmonization & integration, and possessing strong planning & project management skills to ensure timely completion of activities are key aspects of this role. Your primary responsibility will be to deliver the complete secondary sales dashboard encompassing field team productivity, distribution metrics, growth & achievement performance, and new product performance, among others. Additionally, you will be accountable for generating other reports including scheme utilization and distributor holding inventory. To excel in this role, you must have a strong creative mindset to generate ideas that drive DMS awareness and visibility. Proficiency in MIS, Power BI, and other relevant tools is essential. Excellent communication skills are required to engage with distributors and employees for activation and status updates. A B.Tech in Computer Science is preferred, along with prior experience in Salesforce Automation / DMS tools, preferably ChannelKonnect.,

You will be responsible for demonstrating thorough knowledge and a proven record of success in executing various functional and technical aspects of SAP Master Data Governance (MDG) projects following industry best practices. This includes Data Modelling, Process Modelling, UI Modelling, Business Validation Rules Modelling, Derivations, and Data Replication Framework (DRF) and Workflow creations and Maintenance. Your role will require a good understanding of the SAP MDG technical framework, including BADI, BAPI/RFC/FM, Workflows, BRF+, Enterprise Services, IDoc, Floorplan Manager, WebDynPro, Fiori, and MDG API framework. Knowledge of SAP data dictionary tables, views, relationships, and corresponding data architecture for ECC and S/4 HANA for various SAP master and transactional data entities is essential, including excellent functional knowledge for core master data objects like customer, vendor, and material. Hands-on experience in configuring customer, vendor, finance, and product/material master data in MDG is necessary, including data harmonization involving de-duplication, mass changes, and data replication involving Key/Value mapping, SOA Web services, ALE/Idoc. Effective communication with customers and partners to understand specific Enterprise Data needs is a key aspect of this role. You should possess excellent written and verbal communication skills with the ability to impart ideas in technical, business, and user-friendly language. Having an appetite to acquire new knowledge, adapt to, and contribute to fast innovation is important for success in this role. The ideal candidate will have a minimum of 5 years of experience in SAP Master Data Governance (MDG) with at least 2 full cycle implementations. Implementation experience of SAP MDG in key domains such as Customer, Supplier, Material, and Finance Master is required. Hands-on experience with SAP Fiori, SAP MDG mass processing, consolidation, central governance, Workflow, and BRF+ is essential. Experience in RDG is considered an added advantage for this role.,

As a Variant Annotation Curator, your primary responsibility will be to curate, harmonize, and maintain high-quality variant annotation datasets from a variety of public and proprietary sources such as ClinVar, ClinGen, HGDM, CADD, refSeq, REVEL gnomAD, dbSNP, and COSMIC. You will be tasked with developing and implementing pipelines to ensure the harmonization of variant annotations across different formats, nomenclatures, and reference genomes. Standardizing variant representations using HGVS, VCF, and other relevant formats will also be a key aspect of your role. Collaboration with the technical operations team to deliver curated data into customer systems will be essential. Additionally, you will be responsible for performing quality control and validation of variant annotations to uphold data integrity standards. Staying updated with the latest developments in variant annotation standards and tools, as well as understanding differences between annotation database versions, will be crucial for this position. Documenting curation processes and contributing to documentation will also be part of your duties. To qualify for this role, you should have a Master's or Ph.D. in Bioinformatics, Computational Biology, Genomics, or a related field. Strong experience with variant annotation tools and databases is required. Proficiency in ETL/workflow tools like Airflow, Nextflow, and scripting languages such as Python or R is essential. Experience with version control systems like Git, familiarity with genomic data formats (VCF, BED, GFF), and reference genome builds (GRCh37/38) is necessary. Previous experience with data harmonization and integration across heterogeneous sources, knowledge of ontologies, and controlled vocabularies (e.g., ClinVar terms, Sequence Ontology) are also important qualifications. Having excellent problem-solving skills and attention to detail is crucial for success in this role. Additionally, experience with SQL (Postgres), familiarity with Kubernetes architecture, and cloud services like AWS would be advantageous in fulfilling your responsibilities as a Variant Annotation Curator.,

Our Client is looking for Clinical Data Labeling Specialist with a bioscience background to join our development team. This role is critical in supporting our oncology data pipeline by reviewing and labeling clinical data accurately for structured extraction, AI model training, and research initiatives. Candidates with prior experience in oncology data abstraction or data management roles in clinical trials are strongly preferred. Key Responsibilities: Review and label clinical oncology data from medical records, pathology reports, radiology findings, and molecular/genomic profiles. Ensure consistency, accuracy, and completeness in annotation across datasets used for model training and clinical validation. Apply standardized medical coding systems such as ICD-10, ICD-O-3, RxNorm, and HGVS for labeling and data harmonization. Work closely with medical reviewers, data engineers, and AI teams to refine labeling schemes and contribute to dataset quality control. Document and manage audit trails of labeling activities and highlight inconsistencies or data quality issues. Support ongoing data abstraction tasks for internal research and external oncology projects. Required Qualifications: Bachelors or Masters degree in Biosciences , Biotechnology, Life Sciences, or a related healthcare discipline. Familiarity with medical terminologies, particularly in oncology and related clinical domains. Ability to interpret unstructured clinical text and identify key data points (diagnosis, histology, tumor site, biomarkers, etc.). Preferred Qualifications: Experience in oncology data abstraction , working with clinical trials, cancer registries, or data management teams. Working knowledge and practical application of coding standards such as ICD- 10, ICD-O-3 (Oncology), RxNorm (medications), and HGVS (genetic variant notation) . Knowledge of clinical research workflows. Key Skills: Strong attention to detail and commitment to data accuracy. Analytical mindset with the ability to interpret complex clinical data. Effective communication and teamwork in a fast-paced development environment. Proficient in using Excel.